TY - JOUR A1 - Haase, Tobias A1 - Sunkara, Vikram A1 - Kohl, Benjamin A1 - Meier, Carola A1 - Bußmann, Patricia A1 - Becker, Jessica A1 - Jagielski, Michal A1 - von Kleist, Max A1 - Ertel, Wolfgang T1 - Discerning the spatio-temporal disease patterns of surgically induced OA mouse models JF - PLOS One N2 - Osteoarthritis (OA) is the most common cause of disability in ageing societies, with no effective therapies available to date. Two preclinical models are widely used to validate novel OA interventions (MCL-MM and DMM). Our aim is to discern disease dynamics in these models to provide a clear timeline in which various pathological changes occur. OA was surgically induced in mice by destabilisation of the medial meniscus. Analysis of OA progression revealed that the intensity and duration of chondrocyte loss and cartilage lesion formation were significantly different in MCL-MM vs DMM. Firstly, apoptosis was seen prior to week two and was narrowly restricted to the weight bearing area. Four weeks post injury the magnitude of apoptosis led to a 40–60% reduction of chondrocytes in the non-calcified zone. Secondly, the progression of cell loss preceded the structural changes of the cartilage spatio-temporally. Lastly, while proteoglycan loss was similar in both models, collagen type II degradation only occurred more prominently in MCL-MM. Dynamics of chondrocyte loss and lesion formation in preclinical models has important implications for validating new therapeutic strategies. Our work could be helpful in assessing the feasibility and expected response of the DMM- and the MCL-MM models to chondrocyte mediated therapies. Y1 - 2019 U6 - https://doi.org/10.1371/journal.pone.0213734 VL - 14 IS - 4 PB - PLOS One ER - TY - JOUR A1 - Rams, Mona A1 - Conrad, Tim T1 - Dictionary Learning for transcriptomics data reveals type-specific gene modules in a multi-class setting JF - it - Information Technology Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1515/itit-2019-0048 SN - 2196-7032 VL - 62 IS - 3-4 PB - De Gruyter CY - Oldenbourg ER - TY - JOUR A1 - Ray, Sourav A1 - Sunkara, Vikram A1 - Schütte, Christof A1 - Weber, Marcus T1 - How to calculate pH-dependent binding rates for receptor-ligand systems based on thermodynamic simulations with different binding motifs JF - Molecular Simulation N2 - Molecular simulations of ligand–receptor interactions are a computational challenge, especially when their association- (‘on’-rate) and dissociation- (‘off’-rate) mechanisms are working on vastly differing timescales. One way of tackling this multiscale problem is to compute the free-energy landscapes, where molecular dynamics (MD) trajectories are used to only produce certain statistical ensembles. The approach allows for deriving the transition rates between energy states as a function of the height of the activation-energy barriers. In this article, we derive the association rates of the opioids fentanyl and N-(3-fluoro-1-phenethylpiperidin-4-yl)-N-phenyl propionamide (NFEPP) in a μ-opioid receptor by combining the free-energy landscape approach with the square-root-approximation method (SQRA), which is a particularly robust version of Markov modelling. The novelty of this work is that we derive the association rates as a function of the pH level using only an ensemble of MD simulations. We also verify our MD-derived insights by reproducing the in vitro study performed by the Stein Lab. Y1 - 2020 U6 - https://doi.org/10.1080/08927022.2020.1839660 VL - 46 IS - 18 SP - 1443 EP - 1452 PB - Taylor and Francis ER - TY - JOUR A1 - Lindow, Norbert A1 - Brünig, Florian A1 - Dercksen, Vincent J. A1 - Fabig, Gunar A1 - Kiewisz, Robert A1 - Redemann, Stefanie A1 - Müller-Reichert, Thomas A1 - Prohaska, Steffen A1 - Baum, Daniel T1 - Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography JF - bioRxiv N2 - We present a software-assisted workflow for the alignment and matching of filamentous structures across a 3D stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite the problems related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets. Y1 - 2020 U6 - https://doi.org/10.1101/2020.05.28.120899 ER - TY - JOUR A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko ED - Li, Jianning ED - Egger, Jan T1 - Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks BT - First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings JF - Towards the Automatization of Cranial Implant Design in Cranioplasty N2 - We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-speci� c cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By � tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset. Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-64327-0_3 N1 - Best Paper Award VL - 12439 SP - 16 EP - 27 PB - Springer International Publishing ET - 1 ER - TY - JOUR A1 - Oeltze-Jaffra, Steffen A1 - Meuschke, Monique A1 - Neugebauer, Mathias A1 - Saalfeld, Sylvia A1 - Lawonn, Kai A1 - Janiga, Gabor A1 - Hege, Hans-Christian A1 - Zachow, Stefan A1 - Preim, Bernhard T1 - Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends, and Future Challenges JF - Computer Graphics Forum N2 - Simulations and measurements of blood and air flow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis, and treatment of diseases. This survey focuses on three main application areas. (1) Computational Fluid Dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D Phase-Contrast (4D PC) Magnetic Resonance Imaging (MRI) of aortic hemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction, and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties, and providing a more fine-granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature-based visualization, the visual comparison of different simulation results, and the interactive visual analysis of the flow field and derived characteristics. Y1 - 2019 U6 - https://doi.org/10.1111/cgf.13394 VL - 38 IS - 1 SP - 87 EP - 125 PB - Wiley ER - TY - JOUR A1 - Rath, Barbara A1 - Conrad, Tim A1 - Myles, Puja A1 - Alchikh, Maren A1 - Ma, Xiaolin A1 - Hoppe, Christian A1 - Tief, Franziska A1 - Chen, Xi A1 - Obermeier, Patrick A1 - Kisler, Bron A1 - Schweiger, Brunhilde T1 - Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials JF - Expert Review of Anti-infective Therapy N2 - Introduction: Influenza-Like Illness is a leading cause of hospitalization in children. Disease burden due to influenza and other respiratory viral infections is reported on a population level, but clinical scores measuring individual changes in disease severity are urgently needed. Areas covered: We present a composite clinical score allowing individual patient data analyses of disease severity based on systematic literature review and WHO-criteria for uncomplicated and complicated disease. The 22-item ViVI Disease Severity Score showed a normal distribution in a pediatric cohort of 6073 children aged 0–18 years (mean age 3.13; S.D. 3.89; range: 0 to 18.79). Expert commentary: The ViVI Score was correlated with risk of antibiotic use as well as need for hospitalization and intensive care. The ViVI Score was used to track children with influenza, respiratory syncytial virus, human metapneumovirus, human rhinovirus, and adenovirus infections and is fully compliant with regulatory data standards. The ViVI Disease Severity Score mobile application allows physicians to measure disease severity at the point-of care thereby taking clinical trials to the next level. KW - Disease severity KW - influenza-like illness KW - influenza KW - respiratory syncytial virus KW - human metapneumovirus KW - human rhinovirus KW - adenovirus KW - seasonality KW - antivirals KW - clinical trials Y1 - 2017 U6 - https://doi.org/10.1080/14787210.2017.1295847 VL - 15 IS - 6 SP - 545 EP - 568 ER - TY - JOUR A1 - Marzban, Forough A1 - Conrad, Tim A1 - Marban, Pouria A1 - Sodoudi, Sahar T1 - Estimation of the Near-Surface Air Temperature during the Day and Nighttime from MODIS in Berlin, Germany JF - International Journal of Advanced Remote Sensing and GIS N2 - Air temperature (Tair or T2m) is an important climatological variable for forest biosphere processes and climate change research. Due to the low density and the uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Tair. In this study, Tair in Berlin is estimated during the day and night time over six land cover/land use (LC/LU) types by satellite remote sensing data over a large domain and a relatively long period (7 years). Aqua and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair. Twelve environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emissivity31, Emissivity32, altitude, albedo, wind speed, wind direction and air pressure) were selected as predictors. Moreover, a comparison between LST from MODIS Terra and Aqua with daytime and night time air temperatures (Tday, Tnight) was done respectively and in addition, the spatial variability of LST and Tair relationship by applying a varying window size on the MODIS LST grid was examined. An analysis of the relationship between the observed Tair and the spatially averaged remotely sensed LST, indicated that 3 × 3 and 1 × 1 pixel size was the optimal window size for the statistical model estimating Tair from MODIS data during the day and night time, respectively. Three supervised learning methods (Adaptive Neuro Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support vector machine (SVR)) were used to estimate Tair during the day and night time, and their performances were validated by cross-validation for each LC/LU. Moreover, tuning the hyper parameters of some models like SVR and ANN were investigated. For tuning the hyper parameters of SVR, Simulated Annealing (SA) was applied (SA-SVR model) and a multiple-layer feed-forward (MLF) neural networks with three layers and different nodes in hidden layers are used with Levenber-Marquardt back-propagation (LM-BP), in order to achieve higher accuracy in the estimation of Tair. Results indicated that the ANN model achieved better accuracy (RMSE= 2.16°C, MAE = 1.69°C, R2 = 0.95) than SA_SVR model (RMSE= 2.50°C, MAE = 1.92°C, R2 = 0.91) and ANFIS model (RMSE= 2.88°C, MAE= 2.2°C, R2 = 0.89) over six LC/LU during the day and night time. The Q-Q diagram of SA-SVR, ANFIS and NN show that all three models slightly tended to underestimate and overestimate the extreme and low temperatures for all LC/LU classes during the day and night time. The weak performance in the extreme and low temperatures are a consequence of the small number of data in these temperatures. These satisfactory results indicate that this approach is proper for estimating air temperature and spatial window size is an important factor that should be considered in the estimation of air temperature. KW - ANFIS KW - ANN KW - Cross-validation KW - MODIS KW - Simulated annealing KW - SVR Y1 - 2018 U6 - https://doi.org/10.23953/cloud.ijarsg.337 VL - 7 IS - 1 SP - 2478 EP - 2517 ER - TY - JOUR A1 - Koltai, Peter A1 - Wu, Hao A1 - Noé, Frank A1 - Schütte, Christof T1 - Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics JF - Computation Y1 - 2018 U6 - https://doi.org/10.3390/computation6010022 VL - 6 IS - 1 PB - MDPI CY - Basel, Switzerland ER - TY - JOUR A1 - Dibak, Manuel A1 - del Razo, Mauricio J. A1 - de Sancho, David A1 - Schütte, Christof A1 - Noé, Frank T1 - MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations JF - Journal of Chemical Physics N2 - Molecular dynamics (MD) simulations can model the interactions between macromolecules with high spatiotemporal resolution but at a high computational cost. By combining high-throughput MD with Markov state models (MSMs), it is now possible to obtain long time-scale behavior of small to intermediate biomolecules and complexes. To model the interactions of many molecules at large length scales, particle-based reaction-diffusion (RD) simulations are more suitable but lack molecular detail. Thus, coupling MSMs and RD simulations (MSM/RD) would be highly desirable, as they could efficiently produce simulations at large time and length scales, while still conserving the characteristic features of the interactions observed at atomic detail. While such a coupling seems straightforward, fundamental questions are still open: Which definition of MSM states is suitable? Which protocol to merge and split RD particles in an association/dissociation reaction will conserve the correct bimolecular kinetics and thermodynamics? In this paper, we make the first step toward MSM/RD by laying out a general theory of coupling and proposing a first implementation for association/dissociation of a protein with a small ligand (A + B ⇌ C). Applications on a toy model and CO diffusion into the heme cavity of myoglobin are reported. Y1 - 2018 U6 - https://doi.org/10.1063/1.5020294 VL - 148 IS - 21 ER - TY - CHAP A1 - Estacio, Laura A1 - Ehlke, Moritz A1 - Tack, Alexander A1 - Castro-Gutierrez, Eveling A1 - Lamecker, Hans A1 - Mora, Rensso A1 - Zachow, Stefan T1 - Unsupervised Detection of Disturbances in 2D Radiographs T2 - 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) N2 - We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data. Y1 - 2021 U6 - https://doi.org/10.1109/ISBI48211.2021.9434091 SP - 367 EP - 370 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Bayat, Amirhossein A1 - Husseini, Malek E. A1 - Löffler, Maximilian A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Wei, Qingyue A1 - Brown, Kevin A1 - Wolf, Matthias A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images JF - arXiv Y1 - 2020 ER - TY - JOUR A1 - Wulkow, Hanna A1 - Conrad, Tim A1 - Djurdjevac Conrad, Natasa A1 - Müller, Sebastian A. A1 - Nagel, Kai A1 - Schütte, Christof T1 - Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts JF - PLOS One Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0249676 VL - 16 IS - 4 PB - Public Library of Science ER - TY - JOUR A1 - Banisch, Ralf A1 - Djurdjevac Conrad, Natasa A1 - Schütte, Christof T1 - Reactive flows and unproductive cycles for random walks on complex networks JF - The European Physical Journal Special Topics, vol. 224, iss. 12 (2015) pp. 2369-2387 Y1 - 2015 U6 - https://doi.org/10.1140/epjst/e2015-02417-8 ER - TY - GEN A1 - Banisch, Ralf A1 - Djurdjevac Conrad, Natasa A1 - Schütte, Christof T1 - Reactive flows and unproductive cycles for random walks on complex networks N2 - We present a comprehensive theory for analysis and understanding of transition events between an initial set A and a target set B for general ergodic finite-state space Markov chains or jump processes, including random walks on networks as they occur, e.g., in Markov State Modelling in molecular dynamics. The theory allows us to decompose the probability flow generated by transition events between the sets A and B into the productive part that directly flows from A to B through reaction pathways and the unproductive part that runs in loops and is supported on cycles of the underlying network. It applies to random walks on directed networks and nonreversible Markov processes and can be seen as an extension of Transition Path Theory. Information on reaction pathways and unproductive cycles results from the stochastic cycle decomposition of the underlying network which also allows to compute their corresponding weight, thus characterizing completely which structure is used how often in transition events. The new theory is illustrated by an application to a Markov State Model resulting from weakly damped Langevin dynamics where the unproductive cycles are associated with periodic orbits of the underlying Hamiltonian dynamics. T3 - ZIB-Report - 15-19 KW - Complex networks KW - molecular transition networks KW - transition path theory KW - cycle decomposition KW - reactive trajectories KW - Markow State Methods Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54239 SN - 1438-0064 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - von Kleist, Max T1 - Markov Control Processes with Rare State Observation: Theory and Application to Treatment Scheduling in HIV-1 JF - Communications in Mathematical Sciences N2 - Markov Decision Processes (MDP) or Partially Observable MDPs (POMDP) are used for modelling situations in which the evolution of a process is partly random and partly controllable. These MDP theories allow for computing the optimal control policy for processes that can continuously or frequently be observed, even if only partially. However, they cannot be applied if state observation is very costly and therefore rare (in time). We present a novel MDP theory for rare, costly observations and derive the corresponding Bellman equation. In the new theory, state information can be derived for a particular cost after certain, rather long time intervals. The resulting information costs enter into the total cost and thus into the optimization criterion. This approach applies to many real world problems, particularly in the medical context, where the medical condition is examined rather rarely because examination costs are high. At the same time, the approach allows for efficient numerical realization. We demonstrate the usefulness of the novel theory by determining, from the national economic perspective, optimal therapeutic policies for the treatment of the human immunodeficiency virus (HIV) in resource-rich and resource-poor settings. Based on the developed theory and models, we discover that available drugs may not be utilized efficiently in resource-poor settings due to exorbitant diagnostic costs. Y1 - 2014 U6 - https://doi.org/10.4310/CMS.2014.v12.n5.a4 VL - 12 IS - 5 SP - 859 EP - 877 ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Banisch, Ralf A1 - Schütte, Christof T1 - Modularity of Directed Networks: Cycle Decomposition Approach JF - Journal of Computational Dynamics 2 (2015) pp. 1-24 N2 - The problem of decomposing networks into modules (or clusters) has gained much attention in recent years, as it can account for a coarsegrained description of complex systems, often revealing functional subunits of these systems. A variety of module detection algorithms have been proposed, mostly oriented towards finding hard partitionings of undirected networks. Despite the increasing number of fuzzy clustering methods for directed networks, many of these approaches tend to neglect important directional information. In this paper, we present a novel random walk based approach for finding fuzzy partitions of directed, weighted networks, where edge directions play a crucial role in defining how well nodes in a module are interconnected. We will show that cycle decomposition of a random walk process connects the notion of network modules and information transport in a network, leading to a new, symmetric measure of node communication. Finally, we will use this measure to introduce a communication graph, for which we will show that although being undirected it inherits all necessary information about modular structures from the original network. Y1 - 2015 U6 - https://doi.org/10.3934/jcd.2015.2.1 ER - TY - GEN A1 - Wang, Han A1 - Schütte, Christof T1 - Building Markov State Models for Periodically Driven Non-Equilibrium Systems N2 - Recent years have seen an increased interest in non-equilibrium molecular dynamics (NEMD) simulations, especially for molecular systems with periodic forcing by external fields, e.g., in the context of studying effects of electromagnetic radiation on the human body tissue. Lately, an NEMD methods with local thermostating has been proposed that allows for studying non-equilibrium processes in a statistically reliable and thermodynamically consistent way. In this article, we demonstrate how to construct Markov State Models (MSMs) for such NEMD simulations. MSM building has been well-established for systems in equilibrium where MSMs with just a few (macro-)states allow for accurate reproduction of the essential kinetics of the molecular system under consideration. Non-equilibrium MSMs have been lacking so far. The article presents how to construct such MSMs and illustrates their validity and usefulness for the case of conformation dynamics of alanine dipeptide in an external electric field. T3 - ZIB-Report - 14-46 KW - Non-equilibrium KW - Markov states model KW - Alanine dipeptide KW - Electric field KW - Floquet theory Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-53167 SN - 1438-0064 N1 - To appear in: Journal of Chemical Theory and Computation ER - TY - GEN A1 - Gul, Raheem A1 - Schütte, Christof A1 - Bernhard, Stefan T1 - Mathematical modeling and sensitivity analysis of arterial anastomosis in arm arteries N2 - Cardiovascular diseases are one of the major problems in medicine today and the number of patients increases worldwide. To find the most efficient treatment, prior knowledge about function and dysfunction of the cardiovas- cular system is required and methods need to be developed that identify the disease in an early stage. Mathematical modeling is a powerful tool for prediction and investigation of cardiovascular diseases. It has been shown that the Windkessel model, being based on an analogy between electrical circuits and fluid flow, is a simple but effective method to model the human cardiovascular system. In this paper, we have applied parametric local sensitivity analysis (LSA) to a linear elastic model of the arm arteries, to find and rank sensitive param- eters that may be helpful in clinical diagnosis. A computational model for end-to-side anastomosis (superior ulnar collateral anastomosis with posterior ulnar recurrent, SUC-PUR) is carried out to study the effects of some clinically relevant haemodynamic parameters like blood flow resistance and terminal re- sistance on pressure and flow at different locations of the arm artery. In this context, we also discuss the spatio-temporal dependency of local sensitivities. The sensitivities with respect to cardiovascular parameters reveal the flow resistance and diameter of the vessels as most sensitive parameters. These parameters play a key role in diagnosis of severe stenosis and aneurysms. In contrast, wall thickness and elastic modulus are found to be less sensitive. T3 - ZIB-Report - 15-22 KW - computational cardiovascular model KW - cardiovascular parameters KW - sensitivity analysis KW - anastomosis KW - Windkessel model Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54339 SN - 1438-0064 ER - TY - CHAP A1 - Mukhopadhyay, Anirban T1 - Total Variation Random Forest: Fully automatic MRI segmentation in congenital heart disease T2 - RAMBO 2016, HVSMR 2016: Reconstruction, Segmentation, and Analysis of Medical Images Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-52280-7_17 VL - LNCS 10129 SP - 165 EP - 171 ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Morillo, Oscar A1 - Zachow, Stefan A1 - Lamecker, Hans T1 - Robust and Accurate Appearance Models Based on Joint Dictionary Learning Data from the Osteoarthritis Initiative T2 - Lecture Notes in Computer Science, Patch-Based Techniques in Medical Imaging. Patch-MI 2016 N2 - Deformable model-based approaches to 3D image segmentation have been shown to be highly successful. Such methodology requires an appearance model that drives the deformation of a geometric model to the image data. Appearance models are usually either created heuristically or through supervised learning. Heuristic methods have been shown to work effectively in many applications but are hard to transfer from one application (imaging modality/anatomical structure) to another. On the contrary, supervised learning approaches can learn patterns from a collection of annotated training data. In this work, we show that the supervised joint dictionary learning technique is capable of overcoming the traditional drawbacks of the heuristic approaches. Our evaluation based on two different applications (liver/CT and knee/MR) reveals that our approach generates appearance models, which can be used effectively and efficiently in a deformable model-based segmentation framework. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-47118-1_4 VL - 9993 SP - 25 EP - 33 ER - TY - JOUR A1 - Sahu, Manish A1 - Mukhopadhyay, Anirban A1 - Szengel, Angelika A1 - Zachow, Stefan T1 - Addressing multi-label imbalance problem of Surgical Tool Detection using CNN JF - International Journal of Computer Assisted Radiology and Surgery N2 - Purpose: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. Methods: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during Convolutional Neural Network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance run time prediction. Results: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. Conclusion: The analysis on tool imbalance, backed by the empirical results indicates the need and superiority of the proposed framework over state-of-the-art techniques. Y1 - 2017 UR - https://link.springer.com/article/10.1007/s11548-017-1565-x U6 - https://doi.org/10.1007/s11548-017-1565-x N1 - Selected for final oral presentation VL - 12 IS - 6 SP - 1013 EP - 1020 PB - Springer ER - TY - JOUR A1 - Klus, Stefan A1 - Schütte, Christof T1 - Towards tensor-based methods for the numerical approximation of the Perron-Frobenius and Koopman operator JF - Journal of Computational Dynamics N2 - The global behavior of dynamical systems can be studied by analyzing the eigenvalues and corresponding eigenfunctions of linear operators associated with the system. Two important operators which are frequently used to gain insight into the system's behavior are the Perron-Frobenius operator and the Koopman operator. Due to the curse of dimensionality, computing the eigenfunctions of high-dimensional systems is in general infeasible. We will propose a tensor-based reformulation of two numerical methods for computing finite-dimensional approximations of the aforementioned infinite-dimensional operators, namely Ulam's method and Extended Dynamic Mode Decomposition (EDMD). The aim of the tensor formulation is to approximate the eigenfunctions by low-rank tensors, potentially resulting in a significant reduction of the time and memory required to solve the resulting eigenvalue problems, provided that such a low-rank tensor decomposition exists. Typically, not all variables of a high-dimensional dynamical system contribute equally to the system's behavior, often the dynamics can be decomposed into slow and fast processes, which is also reflected in the eigenfunctions. Thus, the weak coupling between different variables might be approximated by low-rank tensor cores. We will illustrate the efficiency of the tensor-based formulation of Ulam's method and EDMD using simple stochastic differential equations. Y1 - 2016 U6 - https://doi.org/10.3934/jcd.2016007 VL - 3 IS - 2 SP - 139 EP - 161 ER - TY - JOUR A1 - Klus, Stefan A1 - Koltai, Peter A1 - Schütte, Christof T1 - On the numerical approximation of the Perron-Frobenius and Koopman operator JF - Journal of Computational Dynamics N2 - Information about the behavior of dynamical systems can often be obtained by analyzing the eigenvalues and corresponding eigenfunctions of linear operators associated with a dynamical system. Examples of such operators are the Perron-Frobenius and the Koopman operator. In this paper, we will review di� fferent methods that have been developed over the last decades to compute � infinite-dimensional approximations of these in� finite-dimensional operators - in particular Ulam's method and Extended Dynamic Mode Decomposition (EDMD) - and highlight the similarities and di� fferences between these approaches. The results will be illustrated using simple stochastic di� fferential equations and molecular dynamics examples. Y1 - 2016 U6 - https://doi.org/10.3934/jcd.2016003 VL - 3 IS - 1 SP - 51 EP - 77 ER - TY - JOUR A1 - Huttary, Rudolf A1 - Goubergrits, Leonid A1 - Schütte, Christof A1 - Bernhard, Stefan T1 - Simulation, Identification and Statistical Variation in Cardiovascular Analysis (SISCA) - a Software Framework for Multi-compartment Lumped Modeling JF - Computers in Biology and Medicine Y1 - 2017 U6 - https://doi.org/10.1016/j.compbiomed.2017.05.021 VL - 87 SP - 104 EP - 123 ER - TY - GEN A1 - Gupta, Pooja A1 - Gramatke, Annika A1 - Einspanier, Ralf A1 - Schütte, Christof A1 - von Kleist, Max A1 - Sharbati, Jutta T1 - In silicio cytotoxicity assessment on cultured rat intestinal cells deduced from cellular impedance measurements N2 - Early and reliable identification of chemical toxicity is of utmost importance. At the same time, reduction of animal testing is paramount. Therefore, methods that improve the interpretability and usability of in vitro assays are essential. xCELLigence’s real-time cell analyzer (RTCA) provides a novel, fast and cost effective in vitro method to probe compound toxicity. We developed a simple mathematical framework for the qualitative and quantitative assessment of toxicity for RTCA measurements. Compound toxicity, in terms of its 50% inhibitory concentration IC_{50} on cell growth, and parameters related to cell turnover were estimated on cultured IEC-6 cells exposed to 10 chemicals at varying concentrations. Our method estimated IC50 values of 113.05, 7.16, 28.69 and 725.15 μM for the apparently toxic compounds 2-acetylamino-fluorene, aflatoxin B1, benzo-[a]-pyrene and chloramphenicol in the tested cell line, in agreement with literature knowledge. IC_{50} values of all apparent in vivo non-toxic compounds were estimated to be non-toxic by our method. Corresponding estimates from RTCA’s in-built model gave false positive (toxicity) predictions in 5/10 cases. Taken together, our proposed method reduces false positive predictions and reliably identifies chemical toxicity based on impedance measurements. The source code for the developed method including instructions is available at https://git.zib.de/bzfgupta/toxfit/tree/master. T3 - ZIB-Report - 17-08 KW - Real-time cell analyzer KW - Toxicity KW - Mathematical modeling KW - IC_{50} Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-62666 SN - 1438-0064 ER - TY - JOUR A1 - Klus, Stefan A1 - Nüske, Feliks A1 - Koltai, Peter A1 - Wu, Hao A1 - Kevrekidis, Ioannis A1 - Schütte, Christof A1 - Noé, Frank T1 - Data-driven model reduction and transfer operator approximation JF - Journal of Nonlinear Science Y1 - 2018 UR - https://link.springer.com/article/10.1007/s00332-017-9437-7 U6 - https://doi.org/10.1007/s00332-017-9437-7 VL - 28 IS - 3 SP - 985 EP - 1010 ER - TY - JOUR A1 - Koltai, Peter A1 - Ciccotti, Giovanni A1 - Schütte, Christof T1 - On Markov state models for non-equilibrium molecular dynamics JF - The Journal of Chemical Physics Y1 - 2016 U6 - https://doi.org/10.1063/1.4966157 N1 - 2016 Editor's Choice of The Journal of Chemical Physics VL - 145 IS - 174103 ER - TY - JOUR A1 - Conrad, Tim A1 - Karsch, K. A1 - Obermeier, Patrick A1 - Seeber, L. A1 - Chen, X. A1 - Tief, Franziska A1 - Muehlhans, S. A1 - Hoppe, Christian A1 - Boettcher, Sindy A1 - Diedrich, S. A1 - Rath, Barbara T1 - Human Parechovirus Infections Associated with Seizures and Rash: A Syndromic Surveillance Study in Children JF - The Pediatric Infectious Disease Journal Y1 - 2015 VL - 34 IS - 10 ER - TY - JOUR A1 - Conrad, Tim A1 - Mireles, Victor T1 - Minimum-overlap clusterings and the sparsity of overcomplete decompositions of binary matrics JF - Procedia Computer Science N2 - Given a set of n binary data points, a widely used technique is to group its features into k clusters. In the case where n {\ensuremath{<}} k, the question of how overlapping are the clusters becomes of interest. In this paper we approach the question through matrix decomposition, and relate the degree of overlap with the sparsity of one of the resulting matrices. We present analytical results regarding bounds on this sparsity, and a heuristic to estimate the minimum amount of overlap that an exact grouping of features into k clusters must have. As shown below, adding new data will not alter this minimum amount of overlap. Y1 - 2015 U6 - https://doi.org/10.1016/j.procs.2015.05.500 VL - 51 SP - 2967 EP - 2971 ER - TY - JOUR A1 - Conrad, Tim A1 - Shao, Borong T1 - Are NoSQL data stores useful for bioinformatics researchers? JF - International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC) Y1 - 2015 U6 - https://doi.org/10.17762/ijritcc2321-8169.1503176 VL - 3 IS - 3 SP - 1704 EP - 1708 ER - TY - JOUR A1 - Conrad, Tim A1 - Leichtle, Alexander Benedikt A1 - Ceglarek, Uta A1 - Weinert, P. A1 - Nakas, C.T. A1 - Nuoffer, Jean-Marc A1 - Kase, Julia A1 - Witzigmann, Helmut A1 - Thiery, Joachim A1 - Fiedler, Georg Martin T1 - Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma JF - Metabolomics N2 - Background: Metabolomics as one of the most rapidly growing technologies in the ?-omics?field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput massspectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. Methods: We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels.  Results: Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Volume under ROC surface (VUS) = 0.891 (95\% CI 0.794 - 0.968)]. Conclusions: We combined highly standardized samples, a three-class study design, a highthroughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and ?despite all its current limitations? can deliver marker panels with high selectivity even in multi-class settings. Y1 - 2013 U6 - https://doi.org/10.1007/s11306-012-0476-7 VL - 9 IS - 3 SP - 677 EP - 687 ER - TY - JOUR A1 - Gupta, Pooja A1 - Reinsch, Norbert A1 - Spötter, Andreas A1 - Conrad, Tim A1 - Bienefeld, Kaspar T1 - Accuracy of the unified approach in maternally influenced traits - illustrated by a simulation study in the honey bee (Apis mellifera) JF - BMC Genetics Y1 - 2013 U6 - https://doi.org/10.1186/1471-2156-14-36 VL - 14 IS - 36 ER - TY - JOUR A1 - Conrad, Tim A1 - You, Xintian T1 - Acfs: accurate circRNA identification and quantification from NGS data JF - Nature Scientific Reports N2 - Circular RNAs (circRNAs) are a group of single-stranded RNAs in closed circular form. They are splicing-generated, widely expressed in various tissues and have functional implications in development and diseases. To facilitate genome-wide characterization of circRNAs using RNA-Seq data, we present a freely available software package named acfs. Acfs allows de novo, accurate and fast identification and abundance quantification of circRNAs from single- and paired-ended RNA-Seq data. On simulated datasets, acfs achieved the highest F1 accuracy and lowest false discovery rate among current state-of-the-art tools. On real-world datasets, acfs efficiently identified more bona fide circRNAs. Furthermore, we demonstrated the power of circRNA analysis on two leukemia datasets. We identified a set of circRNAs that are differentially expressed between AML and APL samples, which might shed light on the potential molecular classification of complex diseases using circRNA profiles. Moreover, chromosomal translocation, as manifested in numerous diseases, could produce not only fusion transcripts but also fusion circRNAs of clinical relevance. Featured with high accuracy, low FDR and the ability to identify fusion circRNAs, we believe that acfs is well suited for a wide spectrum of applications in characterizing the landscape of circRNAs from non-model organisms to cancer biology. Y1 - 2016 U6 - https://doi.org/10.1038/srep38820 VL - 6 ER - TY - JOUR A1 - Hoppe, Christian A1 - Obermeier, Patrick A1 - Mehlhans, S. A1 - Alchikh, Maren A1 - Seeber, L. A1 - Tief, Franziska A1 - Karsch, K. A1 - Chen, X. A1 - Boettcher, Sindy A1 - Diedrich, S. A1 - Conrad, Tim T1 - Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study JF - Drug Safety N2 - Regulatory authorities often receive poorly structured safety reports requiring considerable effort to investigate potential adverse events post hoc. Automated question-and-answer systems may help to improve the overall quality of safety information transmitted to pharmacovigilance agencies. This paper explores the use of the VACC-Tool (ViVI Automated Case Classification Tool) 2.0, a mobile application enabling physicians to classify clinical cases according to 14 pre-defined case definitions for neuroinflammatory adverse events (NIAE) and in full compliance with data standards issued by the Clinical Data Interchange Standards Consortium. METHODS: The validation of the VACC-Tool 2.0 (beta-version) was conducted in the context of a unique quality management program for children with suspected NIAE in collaboration with the Robert Koch Institute in Berlin, Germany. The VACC-Tool was used for instant case classification and for longitudinal follow-up throughout the course of hospitalization. Results were compared to International Classification of Diseases , Tenth Revision (ICD-10) codes assigned in the emergency department (ED). RESULTS: From 07/2013 to 10/2014, a total of 34,368 patients were seen in the ED, and 5243 patients were hospitalized; 243 of these were admitted for suspected NIAE (mean age: 8.5 years), thus participating in the quality management program. Using the VACC-Tool in the ED, 209 cases were classified successfully, 69 \% of which had been missed or miscoded in the ED reports. Longitudinal follow-up with the VACC-Tool identified additional NIAE. CONCLUSION: Mobile applications are taking data standards to the point of care, enabling clinicians to ascertain potential adverse events in the ED setting and during inpatient follow-up. Compliance with Clinical Data Interchange Standards Consortium (CDISC) data standards facilitates data interoperability according to regulatory requirements. Y1 - 2016 U6 - https://doi.org/10.1007/s40264-016-0437-6 VL - 39 IS - 10 SP - 977 EP - 988 ER - TY - CHAP A1 - Shao, Borong A1 - Conrad, Tim T1 - Epithelial Mesenchymal Transition Regulatory Network-based Feature Selection in Lung Cancer Prognosis Prediction T2 - Lecture Notes in Computer Science (LNCS) N2 - Feature selection technique is often applied in identifying cancer prognosis biomarkers. However, many feature selection methods are prone to over-fitting or poor biological interpretation when applied on biological high-dimensional data. Network-based feature selection and data integration approaches are proposed to identify more robust biomarkers. We conducted experiments to investigate the advantages of the two approaches using epithelial mesenchymal transition regulatory network, which is demonstrated as highly relevant to cancer prognosis. We obtained data from The Cancer Genome Atlas. Prognosis prediction was made using Support Vector Machine. Under our experimental settings, the results showed that network-based features gave significantly more accurate predictions than individual molecular features, and features selected from integrated data (RNA-Seq and micro-RNA data) gave significantly more accurate predictions than features selected from single source data (RNA-Seq data). Our study indicated that biological network-based feature transformation and data integration are two useful approaches to identify robust cancer biomarkers. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-31744-1_13 VL - 9656 SP - 1235 EP - 146 ER - TY - JOUR A1 - Tief, Franziska A1 - Hoppe, Christian A1 - Seeber, L. A1 - Obermeier, Patrick A1 - Chen, X. A1 - Karsch, K. A1 - Muehlhans, S. A1 - Adamou, E. A1 - Conrad, Tim A1 - Schweiger, Brunhilde A1 - Adam, T. A1 - Rath, Barbara T1 - An inception cohort study assessing the role of bacterial co-infections in children with influenza and ILI and a clinical decision model for stringent antibiotic use JF - Antiviral Therapy N2 - BACKGROUND: Influenza-like illness (ILI) is a common reason for paediatric consultations. Viral causes predominate, but antibiotics are used frequently. With regard to influenza, pneumococcal coinfections are considered major contributors to morbidity/mortality. METHODS: In the context of a perennial quality management (QM) programme at the Charit{\'e} Departments of Paediatrics and Microbiology in collaboration with the Robert Koch Institute, children aged 0-18 years presenting with signs and symptoms of ILI were followed from the time of initial presentation until hospital discharge (Charit{\'e} Influenza-Like Disease = ChILD Cohort). An independent QM team performed highly standardized clinical assessments using a disease severity score based on World Health Organization criteria for uncomplicated and complicated/progressive disease. Nasopharyngeal and pharyngeal samples were collected for viral reverse transcription polymerase chain reaction and bacterial culture/sensitivity and MaldiTOF analyses. The term 'detection' was used to denote any evidence of viral or bacterial pathogens in the (naso)pharyngeal cavity. With the ChILD Cohort data collected, a standard operating procedure (SOP) was created as a model system to reduce the inappropriate use of antibiotics in children with ILI. Monte Carlo simulations were performed to assess cost-effectiveness. RESULTS: Among 2,569 ChILD Cohort patients enrolled from 12/2010 to 04/2013 (55\% male, mean age 3.2 years, range 0-18, 19\% {\ensuremath{>}}5 years), 411 patients showed laboratory-confirmed influenza, with bacterial co-detection in 35\%. Influenza and pneumococcus were detected simultaneously in 12/2,569 patients, with disease severity clearly below average. Pneumococcal vaccination rates were close to 90\%. Nonetheless, every fifth patient was already on antibiotics upon presentation; new antibiotic prescriptions were issued in an additional 20\%. Simulation of the model SOP in the same dataset revealed that the proposed decision model could have reduced the inappropriate use of antibiotics significantly (P{\ensuremath{<}}0.01) with an incremental cost-effectiveness ratio of -99.55?. CONCLUSIONS: Physicians should be made aware that in times of pneumococcal vaccination the prevalence and severity of influenza infections complicated by pneumococci may decline. Microbiological testing in combination with standardized disease severity assessments and review of vaccination records could be cost-effective, as well as promoting stringent use of antibiotics and a personalized approach to managing children with ILI. Y1 - 2016 U6 - https://doi.org/10.3851/IMP3034 VL - 21 SP - 413 EP - 424 ER - TY - JOUR A1 - Obermeier, Patrick A1 - Muehlhans, S. A1 - Hoppe, Christian A1 - Karsch, K. A1 - Tief, Franziska A1 - Seeber, L. A1 - Chen, X. A1 - Conrad, Tim A1 - Boettcher, Sindy A1 - Diedrich, S. A1 - Rath, Barbara T1 - Enabling Precision Medicine With Digital Case Classification at the Point-of-Care JF - EBioMedicine N2 - Infectious and inflammatory diseases of the central nervous system are difficult to identify early. Case definitions for aseptic meningitis, encephalitis, myelitis, and acute disseminated encephalomyelitis (ADEM) are available, but rarely put to use. The VACC-Tool (Vienna Vaccine Safety Initiative Automated Case Classification-Tool) is a mobile application enabling immediate case ascertainment based on consensus criteria at the point-of-care. The VACC-Tool was validated in a quality management program in collaboration with the Robert-Koch-Institute. Results were compared to ICD-10 coding and retrospective analysis of electronic health records using the same case criteria. Of 68,921 patients attending the emergency room in 10/2010-06/2013, 11,575 were hospitalized, with 521 eligible patients (mean age: 7.6 years) entering the quality management program. Using the VACC-Tool at the point-of-care, 180/521 cases were classified successfully and 194/521 ruled out with certainty. Of the 180 confirmed cases, 116 had been missed by ICD-10 coding, 38 misclassified. By retrospective application of the same case criteria, 33 cases were missed. Encephalitis and ADEM cases were most likely missed or misclassified. The VACC-Tool enables physicians to ask the right questions at the right time, thereby classifying cases consistently and accurately, facilitating translational research. Future applications will alert physicians when additional diagnostic procedures are required. Y1 - 2016 U6 - https://doi.org/10.1016/j.ebiom.2016.01.008 VL - 4 SP - 191 EP - 196 ER - TY - JOUR A1 - Conrad, Tim A1 - Bruckner, Sharon A1 - Kayser, Bastian T1 - Finding Modules in Networks with Non-modular Regions JF - Lecture Notes in Computer Science (Proceedings of SEA 2013) N2 - Most network clustering methods share the assumption that the network can be completely decomposed into modules, that is, every node belongs to (usually exactly one) module. Forcing this constraint can lead to misidentification of modules where none exist, while the true modules are drowned out in the noise, as has been observed e.g. for protein interaction networks. We thus propose a clustering model where networks contain both a modular region consisting of nodes that can be partitioned into modules, and a transition region containing nodes that lie between or outside modules. We propose two scores based on spectral properties to determine how well a network fits this model. We then evaluate three (partially adapted) clustering algorithms from the literature on random networks that fit our model, based on the scores and comparison to the ground truth. This allows to pinpoint the types of networks for which the different algorithms perform well. Y1 - 2013 U6 - https://doi.org/10.1007/978-3-642-38527-8_18 VL - 7933 SP - 188 EP - 199 ER - TY - JOUR A1 - Conrad, Tim A1 - Rath, Barbara A1 - Tief, Franziska A1 - Karsch, K. A1 - Muehlhans, S. A1 - Obermeier, Patrick A1 - Adamou, E. A1 - Chen, X. A1 - Seeber, L. A1 - Peiser, Ch. A1 - Hoppe, Christian A1 - von Kleist, Max A1 - Schweiger, Brunhilde T1 - Towards a personalized approach to managing of influenza infections in infants and children - food for thought and a note on oseltamivir JF - Infectious Disorders - Drug Targets Y1 - 2013 VL - 13 IS - 1 SP - 25 EP - 33 ER - TY - JOUR A1 - Conrad, Tim A1 - Leichtle, Alexander Benedikt A1 - Nuoffer, Jean-Marc A1 - Ceglarek, Uta A1 - Kase, Julia A1 - Witzigmann, Helmut A1 - Thiery, Joachim A1 - Fiedler, Georg Martin T1 - Serum amino acid profiles and their alterations in colorectal cancer JF - Metabolomics N2 - Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95\% CI 0.815?0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls. Y1 - 2012 U6 - https://doi.org/10.1007/s11306-011-0357-5 ER - TY - JOUR A1 - Gupta, Pooja A1 - Conrad, Tim A1 - Spötter, Andreas A1 - Reinsch, Norbert A1 - Bienefeld, Kaspar T1 - Simulating a base population in honey bee for molecular genetic studies JF - Genetics Selection Evolution N2 - Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (Varroa destructor) is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding values estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has distinct genetic and reproductive biology characteristics. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee.  Results: Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1) the position of markers on each chromosome, (2) allele frequency, (3) ?2 statistics for Hardy- Weinberg equilibrium, (4) a sorted list of markers with a minor allele frequency less than or equal to the input value, (5) average r2 values of linkage disequilibrium between all simulated marker loci pair for all generations and (6) average r2 value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency. Conclusion: We developed a software program that takes into account the genetic and reproductive biology characteristics specific to the honey bee and that can be used to constitute a genomic dataset compatible with the simulation studies necessary to optimize breeding programs. The source code together with an instruction file is freely accessible at http://msproteomics.org/Research/Misc/honeybeepopulationsimulator.html Y1 - 2012 U6 - https://doi.org/10.1186/1297-9686-44-14 ER - TY - JOUR A1 - Conrad, Tim T1 - New Appraches for Visualizing and Analyzing Metabolic Pathways JF - Proceedings of the Second Australian Undergraduate Students? Computing Conference N2 - Visualizing of metabolic pathways (or networks) has been done by many differentapproaches. In this work, we implemented and tested existing graph layout algorithms, and present a new approach to lay-out medium size metabolic pathways (500-20,000 vertices) by implementing and combining three well known graph lay-out algorithms (high dimension embedding, spring-embedder preprocessing, spring-embedder), through 3D space density analysis facilitated by the Octree technique. For the analysis of the results of metabolic pathways simulations we present two new techniques: rstly, a powerful technique to visualize pathways simulation data was created to unveil and understand concentration ows through metabolic pathways. This was achieved by mapping the color encoded concentration value of every substance from each time step of the simulation to its graphical representation in the layout. By combining all resulting images (from each time step) and displaying them as a movie, many characteristics such as subnetworks, alternative routes through the network, and differences between a modied pathway and its unmodied version can be revealed. Secondly, a new method to detect co-regulated substances in metabolic pathways and to recognize differences between two versions of a pathway, was established. To do this, we transformed the simulation data into a row-based representation, color-coded these rows, and reordered them with respect to similarity by using a Genetic Algorithm variant. From the arising discrete 2-dimensional matrix consisting of concentration values, a continuous 2-dimensional fourier row function was computed. This function can be used to measure properties, such as similarities in a pathway between time steps, or substances, or to detect and evaluate differences between modied versions of the same pathway. Y1 - 2004 ER - TY - JOUR A1 - Gelß, Patrick A1 - Matera, Sebastian A1 - Schütte, Christof T1 - Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model JF - Journal of Computational Physics N2 - In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO2(110) surface. We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased Y1 - 2016 U6 - https://doi.org/10.1016/j.jcp.2016.03.025 VL - 314 SP - 489 EP - 502 ER - TY - JOUR A1 - Vega, Iliusi A1 - Schütte, Christof A1 - Conrad, Tim T1 - Finding metastable states in real-world time series with recurrence networks JF - Physica A: Statistical Mechanics and its Applications N2 - In the framework of time series analysis with recurrence networks, we introduce a self-adaptive method that determines the elusive recurrence threshold and identifies metastable states in complex real-world time series. As initial step, we introduce a way to set the embedding parameters used to reconstruct the state space from the time series. We set them as the ones giving the maximum Shannon entropy of the diagonal line length distribution for the first simultaneous minima of recurrence rate and Shannon entropy. To identify metastable states, as well as the transitions between them, we use a soft partitioning algorithm for module finding which is specifically developed for the case in which a system shows metastability. We illustrate our method with a complex time series example. Finally, we show the robustness of our method for identifying metastable states. Our results suggest that our method is robust for identifying metastable states in complex time series, even when introducing considerable levels of noise and missing data points. Y1 - 2016 U6 - https://doi.org/10.1016/j.physa.2015.10.041 VL - 445 SP - 1 EP - 17 ER - TY - JOUR A1 - Conrad, Tim A1 - Genzel, Martin A1 - Cvetkovic, Nada A1 - Wulkow, Niklas A1 - Leichtle, Alexander Benedikt A1 - Vybiral, Jan A1 - Kytyniok, Gitta A1 - Schütte, Christof T1 - Sparse Proteomics Analysis – a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data JF - BMC Bioinfomatics N2 - Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA),based on thet heory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets. Y1 - 2017 U6 - https://doi.org/10.1186/s12859-017-1565-4 VL - 18 IS - 160 ER - TY - JOUR A1 - Gupta, Pooja A1 - Gramatke, Annika A1 - Einspanier, Ralf A1 - Schütte, Christof A1 - von Kleist, Max A1 - Sharbati, Jutta T1 - In silico cytotoxicity assessment on cultured rat intestinal cells deduced from cellular impedance measurements JF - Toxicology in Vitro N2 - Early and reliable identification of chemical toxicity is of utmost importance. At the same time, reduction of animal testing is paramount. Therefore, methods that improve the interpretability and usability of in vitro assays are essential. xCELLigence’s real-time cell analyzer (RTCA) provides a novel, fast and cost effective in vitro method to probe compound toxicity. We developed a simple mathematical framework for the qualitative and quantitative assessment of toxicity for RTCA measurements. Compound toxicity, in terms of its 50% inhibitory concentration IC50 on cell growth, and parameters related to cell turnover were estimated on cultured IEC-6 cells exposed to 10 chemicals at varying concentrations. Our method estimated IC50 values of 113.05, 7.16, 28.69 and 725.15 μM for the apparently toxic compounds 2-acetylamino-fluorene, aflatoxin B1, benzo-[a]-pyrene and chloramphenicol in the tested cell line, in agreement with literature knowledge. IC50 values of all apparent in vivo non-toxic compounds were estimated to be non-toxic by our method. Corresponding estimates from RTCA’s in-built model gave false positive (toxicity) predictions in 5/10 cases. Taken together, our proposed method reduces false positive predictions and reliably identifies chemical toxicity based on impedance measurements. The source code for the developed method including instructions is available at https://git.zib.de/bzfgupta/toxfit/tree/master. KW - Real-time cell analyzer KW - Toxicity KW - Mathematical modeling Y1 - 2017 SN - 1438-0064 VL - 41 SP - 179 EP - 188 ER - TY - JOUR A1 - Wilson, David A1 - Anglin, Carolyn A1 - Ambellan, Felix A1 - Grewe, Carl Martin A1 - Tack, Alexander A1 - Lamecker, Hans A1 - Dunbar, Michael A1 - Zachow, Stefan T1 - Validation of three-dimensional models of the distal femur created from surgical navigation point cloud data for intraoperative and postoperative analysis of total knee arthroplasty JF - International Journal of Computer Assisted Radiology and Surgery N2 - Purpose: Despite the success of total knee arthroplasty there continues to be a significant proportion of patients who are dissatisfied. One explanation may be a shape mismatch between pre and post-operative distal femurs. The purpose of this study was to investigate a method to match a statistical shape model (SSM) to intra-operatively acquired point cloud data from a surgical navigation system, and to validate it against the pre-operative magnetic resonance imaging (MRI) data from the same patients. Methods: A total of 10 patients who underwent navigated total knee arthroplasty also had an MRI scan less than 2 months pre-operatively. The standard surgical protocol was followed which included partial digitization of the distal femur. Two different methods were employed to fit the SSM to the digitized point cloud data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). The available MRI data were manually segmented and the reconstructed three-dimensional surfaces used as ground truth against which the statistical shape model fit was compared. Results: For both approaches, the difference between the statistical shape model-generated femur and the surface generated from MRI segmentation averaged less than 1.7 mm, with maximum errors occurring in less clinically important areas. Conclusion: The results demonstrated good correspondence with the distal femoral morphology even in cases of sparse data sets. Application of this technique will allow for measurement of mismatch between pre and post-operative femurs retrospectively on any case done using the surgical navigation system and could be integrated into the surgical navigation unit to provide real-time feedback. Y1 - 2017 UR - https://link.springer.com/content/pdf/10.1007%2Fs11548-017-1630-5.pdf U6 - https://doi.org/10.1007/s11548-017-1630-5 VL - 12 IS - 12 SP - 2097 EP - 2105 PB - Springer ER - TY - JOUR A1 - von Tycowicz, Christoph A1 - Ambellan, Felix A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - An Efficient Riemannian Statistical Shape Model using Differential Coordinates JF - Medical Image Analysis N2 - We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidean structure. A key advantage of our framework is that statistics in a manifold shape space becomes numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidean approach in terms of shape-based classification of morphological disorders. Y1 - 2018 U6 - https://doi.org/10.1016/j.media.2017.09.004 VL - 43 IS - 1 SP - 1 EP - 9 ER - TY - JOUR A1 - Benn, Andreas A1 - Hiepen, Christian A1 - Osterland, Marc A1 - Schütte, Christof A1 - Zwijsen, An A1 - Knaus, Petra T1 - Role of bone morphogenetic proteins in sprouting angiogenesis: differential BMP receptor-dependent signaling pathways balance stalk vs. tip cell competence JF - FASEB Journal N2 - Before the onset of sprouting angiogenesis, the endothelium is prepatterned for the positioning of tip and stalk cells. Both cell identities are not static, as endothelial cells (ECs) constantly compete for the tip cell position in a dynamic fashion. Here, we show that both bone morphogenetic protein (BMP) 2 and BMP6 are proangiogenic in vitro and ex vivo and that the BMP type I receptors, activin receptor-like kinase (ALK)3 and ALK2, play crucial and distinct roles in this process. BMP2 activates the expression of tip cell–associated genes, such as DLL4 (delta-like ligand 4) and KDR (kinase insert domain receptor), and p38-heat shock protein 27 (HSP27)–dependent cell migration, thereby generating tip cell competence. Whereas BMP6 also triggers collective cell migration via the p38-HSP27 signaling axis, BMP6 induces in addition SMAD1/5 signaling, thereby promoting the expression of stalk cell–associated genes, such as HES1 (hairy and enhancer of split 1) and FLT1 (fms-like tyrosine kinase 1). Specifically, ALK3 is required for sprouting from HUVEC spheroids, whereas ALK2 represses sprout formation. We demonstrate that expression levels and respective complex formation of BMP type I receptors in ECs determine stalk vs. tip cell identity, thus contributing to endothelial plasticity during sprouting angiogenesis. As antiangiogenic monotherapies that target the VEGF or ALK1 pathways have not fulfilled efficacy objectives in clinical trials, the selective targeting of the ALK2/3 pathways may be an attractive new approach. Y1 - 2017 U6 - https://doi.org/10.1096/fj.201700193RR VL - 31 IS - 11 SP - 4720 EP - 4733 ER - TY - JOUR A1 - Winkelmann, Stefanie A1 - Schütte, Christof T1 - Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems JF - The Journal of Chemical Physics N2 - Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations. Y1 - 2017 U6 - https://doi.org/10.1063/1.4986560 VL - 147 IS - 11 SP - 114115-1 EP - 114115-18 ER - TY - GEN A1 - Schütte, Christof A1 - Conrad, Tim ED - Deuflhard, Peter ED - Grötschel, Martin ED - Hömberg, Dietmar ED - Horst, Ulrich ED - Kramer, Jürg ED - Mehrmann, Volker ED - Polthier, Konrad ED - Schmidt, Frank ED - Skutella, Martin ED - Sprekels, Jürgen T1 - Showcase 3: Information-based medicine T2 - MATHEON-Mathematics for Key Technologies Y1 - 2014 VL - 1 SP - 66 EP - 67 PB - European Mathematical Society ER - TY - JOUR A1 - Kryven, Ivan A1 - Röblitz, Susanna A1 - Schütte, Christof T1 - Solution of the chemical master equation by radial basis functions approximation with interface tracking JF - BMC Systems Biology N2 - Background. The chemical master equation is the fundamental equation of stochastic chemical kinetics. This differential-difference equation describes temporal evolution of the probability density function for states of a chemical system. A state of the system, usually encoded as a vector, represents the number of entities or copy numbers of interacting species, which are changing according to a list of possible reactions. It is often the case, especially when the state vector is high-dimensional, that the number of possible states the system may occupy is too large to be handled computationally. One way to get around this problem is to consider only those states that are associated with probabilities that are greater than a certain threshold level. Results. We introduce an algorithm that significantly reduces computational resources and is especially powerful when dealing with multi-modal distributions. The algorithm is built according to two key principles. Firstly, when performing time integration, the algorithm keeps track of the subset of states with significant probabilities (essential support). Secondly, the probability distribution that solves the equation is parametrised with a small number of coefficients using collocation on Gaussian radial basis functions. The system of basis functions is chosen in such a way that the solution is approximated only on the essential support instead of the whole state space. Discussion. In order to demonstrate the effectiveness of the method, we consider four application examples: a) the self-regulating gene model, b) the 2-dimensional bistable toggle switch, c) a generalisation of the bistable switch to a 3-dimensional tristable problem, and d) a 3-dimensional cell differentiation model that, depending on parameter values, may operate in bistable or tristable modes. In all multidimensional examples the manifold containing the system states with significant probabilities undergoes drastic transformations over time. This fact makes the examples especially challenging for numerical methods. Conclusions. The proposed method is a new numerical approach permitting to approximately solve a wide range of problems that have been hard to tackle until now. A full representation of multi-dimensional distributions is recovered. The method is especially attractive when dealing with models that yield solutions of a complex structure, for instance, featuring multi-stability. Electronic version: http://www.biomedcentral.com/1752-0509/9/67 Y1 - 2015 U6 - https://doi.org/10.1186/s12918-015-0210-y VL - 9 IS - 67 SP - 1 EP - 12 ER - TY - CHAP A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Statistical Shape Modeling of Musculoskeletal Structures and Its Applications T2 - Computational Radiology for Orthopaedic Interventions N2 - Statistical shape models (SSM) describe the shape variability contained in a given population. They are able to describe large populations of complex shapes with few degrees of freedom. This makes them a useful tool for a variety of tasks that arise in computer-aided madicine. In this chapter we are going to explain the basic methodology of SSMs and present a variety of examples, where SSMs have been successfully applied. Y1 - 2016 SN - 978-3-319-23481-6 U6 - https://doi.org/10.1007/978-3-319-23482-3 VL - 23 SP - 1 EP - 23 PB - Springer ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MRI T2 - Functional Imaging and Modeling of the Heart N2 - Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP- BOLD) MR is capable of diagnosing an ongoing ischemia by detecting changes in myocardial intensity patterns at rest without any contrast and stress agents. Visualizing and detecting these changes require significant post-processing, including myocardial segmentation for isolating the myocardium. But, changes in myocardial intensity pattern and myocardial shape due to the heart’s motion challenge automated standard CINE MR myocardial segmentation techniques resulting in a significant drop of segmentation accuracy. We hypothesize that the main reason behind this phenomenon is the lack of discernible features. In this paper, a multi scale discriminative dictionary learning approach is proposed for supervised learning and sparse representation of the myocardium, to improve the myocardial feature selection. The technique is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canine subjects. The proposed method significantly outperforms standard cardiac segmentation techniques, including segmentation via registration, level sets and supervised methods for myocardial segmentation. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-20309-6_22 VL - 9126 SP - 189 EP - 197 PB - Springer ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Unsupervised myocardial segmentation for cardiac MRI T2 - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 N2 - Though unsupervised segmentation was a de-facto standard for cardiac MRI segmentation early on, recently cardiac MRI segmentation literature has favored fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR modalities. For example, CP-BOLD is a new MRI technique that has been shown to detect ischemia without any contrast at stress but also at rest conditions. Although CP-BOLD looks similar to standard CINE, changes in myocardial intensity patterns and shape across cardiac phases, due to the heart’s motion, BOLD effect and artifacts affect the underlying mechanisms of fully supervised segmentation techniques resulting in a significant drop in segmentation accuracy. In this paper, we present a fully unsupervised technique for segmenting myocardium from the background in both standard CINE MR and CP-BOLD MR. We combine appearance with motion information (obtained via Optical Flow) in a dictionary learning framework to sparsely represent important features in a low dimensional space and separate myocardium from background accordingly. Our fully automated method learns background-only models and one class classifier provides myocardial segmentation. The advantages of the proposed technique are demonstrated on a dataset containing CP-BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects, where our method outperforms state-of-the-art supervised segmentation techniques in CP-BOLD MR and performs at-par for standard CINE MR. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-24574-4_2 VL - LNCS 9351 SP - 12 EP - 20 ER - TY - CHAP A1 - Oksuz, Ilkay A1 - Mukhopadhyay, Anirban A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR T2 - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 N2 - Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP- BOLD) MRI is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. The precise registration among the cardiac phases in this cine type acquisition is essential for automating the analysis of images of this technique, since it can potentially lead to better specificity of ischemia detection. However, inconsistency in myocardial intensity patterns and the changes in myocardial shape due to the heart’s motion lead to low registration performance for state- of-the-art methods. This low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric defini- tions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictionary learning approach for source and target images, are used to improve myocardial registration. This method combines appearance with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low dimensional space. The sum of squared differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed descriptor is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-24571-3_25 VL - 9350 SP - 205 EP - 213 PB - Springer ER - TY - GEN A1 - Osterland, Marc A1 - Benn, Andreas A1 - Prohaska, Steffen A1 - Schütte, Christof T1 - Single Cell Tracking in Phase-Contrast Microscopy T2 - EMBL Symposium 2015 - Seeing is Believing - Imaging the Processes of Life N2 - In this work, we developed an automatic algorithm to analyze cell migration in chemotaxis assays, based on phase-contrast time-lapse microscopy. While manual approaches are still widely used in recent publications, our algorithm is able to track hundreds of single cells per frame. The extracted paths are analysed with traditional geometrical approaches as well as diffusion-driven Markov state models (MSM). Based on these models, a detailed view on spatial and temporal effects is possible. Using our new approach on experimental data, we are able to distinguish between directed migration (e.g. towards a VEGF gradient) and random migration without favored direction. A calculation of the committor probabilities reveals that cells of the whole image area are more likely to migrate directly towards the VEGF than away from it during the first four hours. However, in absence of a chemoattractant, cells migrate more likely to their nearest image border. These conclusions are supported by the spatial mean directions. In a next step, the cell-cell interaction during migration and the migration of cell clusters will be analyzed. Furthermore, we want to observe phenotypical changes during migration based on fluorescence microscopy and machine learning. The algorithm is part of a collaborative platform which brings the experimental expertise of scientists from life sciences and the analytical knowledge of computer scientists together. This platform is built using web-based technologies with a responsive real-time user interface. All data, including raw and metadata as well as the accompanying results, will be stored in a secure and scalable compute cluster. The compute cluster provides sufficient space and computational power for modern image-based experiments and their analyses. Specific versions of data and results can be tagged to keep immutable records for archival. Y1 - 2015 ER - TY - GEN A1 - Koltai, Peter A1 - Ciccotti, Giovanni A1 - Schütte, Christof T1 - On metastability and Markov state models for non-stationary molecular dynamics BT - 2016 Editor's Choice of The Journal of Chemical Physics T2 - The Journal of Chemical Physics N2 - We utilize the theory of coherent sets to build Markov state models for non- equilibrium molecular dynamical systems. Unlike for systems in equilibrium, “meta- stable” sets in the non-equilibrium case may move as time evolves. We formalize this concept by relying on the theory of coherent sets, based on this we derive finite-time non-stationary Markov state models, and illustrate the concept and its main differences to equilibrium Markov state modeling on simple, one-dimensional examples. T3 - ZIB-Report - 16-11 KW - coherent set, KW - Markov state model KW - non-equilibrium molecular dynamics KW - metastability Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-57869 SN - 1438-0064 VL - 174103 ET - 145 ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Weber, Marcus A1 - Schütte, Christof T1 - Finding dominant structures of nonreversible Markov processes JF - Multiscale Modeling and Simulation Y1 - 2016 U6 - https://doi.org/10.1137/15M1032272 VL - 14 IS - 4 SP - 1319 EP - 1340 ER - TY - GEN A1 - Mukhopadhyay, Anirban A1 - Kumar, Arun A1 - Bhandarkar, Suchendra T1 - Joint Geometric Graph Embedding for Partial Shape Matching in Images T2 - IEEE Winter Conference on Applications of Computer Vision N2 - A novel multi-criteria optimization framework for matching of partially visible shapes in multiple images using joint geometric graph embedding is proposed. The proposed framework achieves matching of partial shapes in images that exhibit extreme variations in scale, orientation, viewpoint and illumination and also instances of occlusion; conditions which render impractical the use of global contour-based descriptors or local pixel-level features for shape matching. The proposed technique is based on optimization of the embedding distances of geometric features obtained from the eigenspectrum of the joint image graph, coupled with regularization over values of the mean pixel intensity or histogram of oriented gradients. It is shown to obtain successfully the correspondences denoting partial shape similarities as well as correspondences between feature points in the images. A new benchmark dataset is proposed which contains disparate image pairs with extremely challenging variations in viewing conditions when compared to an existing dataset [18]. The proposed technique is shown to significantly outperform several state-of-the-art partial shape matching techniques on both datasets. Y1 - 2016 SP - 1 EP - 9 PB - IEEE ET - IEEE Winter Conference on Applications of Computer Vision (WACV) ER - TY - CHAP A1 - Gupta, Pooja A1 - Krause, Carola A1 - Rikeit, Paul A1 - Röblitz, Susanna A1 - Knaus, Petra A1 - Schütte, Christof T1 - Modeling of the BMP mediated co-regulation of the Smad and Non-Smad pathways in the context of cell density T2 - 10th International BMP conference, 2014, Berlin, Germany Y1 - 2014 ER - TY - JOUR A1 - Peppert, Felix A1 - von Kleist, Max A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks JF - IEEE Transactions on Neural Networks and Learning Systems N2 - Deep convolutional neural networks (DCNNs) are routinely used for image segmentation of biomedical data sets to obtain quantitative measurements of cellular structures like tissues. These cellular structures often contain gaps in their boundaries, leading to poor segmentation performance when using DCNNs like the U-Net. The gaps can usually be corrected by post-hoc computer vision (CV) steps, which are specific to the data set and require a disproportionate amount of work. As DCNNs are Universal Function Approximators, it is conceivable that the corrections should be obsolete by selecting the appropriate architecture for the DCNN. In this article, we present a novel theoretical framework for the gap-filling problem in DCNNs that allows the selection of architecture to circumvent the CV steps. Combining information-theoretic measures of the data set with a fundamental property of DCNNs, the size of their receptive field, allows us to formulate statements about the solvability of the gap-filling problem independent of the specifics of model training. In particular, we obtain mathematical proof showing that the maximum proficiency of filling a gap by a DCNN is achieved if its receptive field is larger than the gap length. We then demonstrate the consequence of this result using numerical experiments on a synthetic and real data set and compare the gap-filling ability of the ubiquitous U-Net architecture with variable depths. Our code is available at https://github.com/ai-biology/dcnn-gap-filling. Y1 - 2022 U6 - https://doi.org/10.1109/TNNLS.2021.3072746 VL - 33 IS - 11 SP - 6194 EP - 6205 ER - TY - JOUR A1 - Li, Jianning A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko A1 - Shi, Haochen A1 - Chen, Xiaojun A1 - Matzkin, Franco A1 - Newcombe, Virginia A1 - Ferrante, Enzo A1 - Jin, Yuan A1 - Ellis, David G. A1 - Aizenberg, Michele R. A1 - Kodym, Oldrich A1 - Spanel, Michal A1 - Herout, Adam A1 - Mainprize, James G. A1 - Fishman, Zachary A1 - Hardisty, Michael R. A1 - Bayat, Amirhossein A1 - Shit, Suprosanna A1 - Wang, Bomin A1 - Liu, Zhi A1 - Eder, Matthias A1 - Pepe, Antonio A1 - Gsaxner, Christina A1 - Alves, Victor A1 - Zefferer, Ulrike A1 - von Campe, Cord A1 - Pistracher, Karin A1 - Schäfer, Ute A1 - Schmalstieg, Dieter A1 - Menze, Bjoern H. A1 - Glocker, Ben A1 - Egger, Jan T1 - AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design JF - IEEE Transactions on Medical Imaging N2 - The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. Y1 - 2021 U6 - https://doi.org/10.1109/TMI.2021.3077047 SN - 0278-0062 VL - 40 IS - 9 SP - 2329 EP - 2342 ER - TY - JOUR A1 - Dai, Chengxin A1 - Füllgrabe, Anja A1 - Pfeuffer, Julianus A1 - Solovyeva, Elizaveta M. A1 - Deng, Jingwen A1 - Moreno, Pablo A1 - Kamatchinathan, Selvakumar A1 - Kundu, Deepti Jaiswal A1 - George, Nancy A1 - Fexovy, Silvie A1 - Grüning, Björn A1 - Föll, Melanie Christine A1 - Griss, Johannes A1 - Vaudel, Marc A1 - Audain, Enrique A1 - Locard-Paulet, Marie A1 - Turewicz, Michael A1 - Eisenacher, Martin A1 - Uszkoreit, Julian A1 - Van Den Bossche, Tim A1 - Schwämmle, Veit A1 - Webel, Henry A1 - Schulze, Stefan A1 - Bouyssié, David A1 - Jayaram, Savita A1 - Duggineni, Vinay Kumar A1 - Samaras, Patroklos A1 - Wilhelm, Mathias A1 - Choi, Meena A1 - Wang, Mingxun A1 - Kohlbacher, Oliver A1 - Brazma, Alvis A1 - Papatheodorou, Irene A1 - Bandeira, Nuno A1 - Deutsch, Eric W. A1 - Vizcaíno, Juan Antonio A1 - Bai, Mingze A1 - Sachsenberg, Timo A1 - Levitsky, Lev I. A1 - Perez-Riverol, Yasset T1 - A proteomics sample metadata representation for multiomics integration and big data analysis JF - Nature Communications N2 - The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1038/s41467-021-26111-3 VL - 12 IS - 5854 ER - TY - JOUR A1 - Umer, Husen M. A1 - Zhu, Yafeng A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Lehtiö, Janne A1 - Branca, Rui A1 - Perez-Riverol, Yasset T1 - Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides JF - Bioinformatics N2 - We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs, and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD, and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling, notably optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we perform a reanalysis of four public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to more than 10% of the total number of peptides identified (43,501 out of 402,512). Y1 - 2021 IS - 5 SP - 1470 EP - 1472 PB - Oxford Academic ET - 38 ER - TY - JOUR A1 - Tack, Alexander A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative JF - PLOS One N2 - Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN- based approaches. To foster such developments we make all segmentations publicly available together with this publication. Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0258855 VL - 16 IS - 10 ER - TY - THES A1 - Krause, Jan T1 - Investigation of Options to Handle 3D MRI Data via Convolutional Neural Networks Application in Knee Osteoarthritits Classification KW - Machine Learning KW - Computational Diagnosis KW - Knee Osteoarthritis Y1 - 2021 ER - TY - THES A1 - Shestakov, Alexey T1 - A Deep Learning Method for Automated Detection of Meniscal Tears in Meniscal Sub-Regions in 3D MRI Data N2 - This work presents a fully automated pipeline, centered around a deep neural network, as well as a method to train that network in an efficient manner, that enables accurate detection of lesions in meniscal anatomical subregions. The network architecture is based on a transformer encoder/decoder. It is trained on DESS and tuned on IW TSE 3D MRI scans sourced from the Osteoarthritis Initiative. Furthermore, it is trained in a multilabel, and multitask fashion, using an auxiliary detection head. The former enables implicit localisation of meniscal defects, that to the best of my knowledge, has not yet been reported elsewhere. The latter enables efficient learning on the entire 3D MRI volume. Thus, the proposed method does not require any expert knowledge at inference. Aggregated inference results from two datasets resulted in an overall AUCROC result of 0.90, 0.91 and 0.93 for meniscal lesion detection anywhere in the knee, in medial and in lateral menisci respectively. These results compare very well to the related work, even though only a fraction of the data has been utilized. Clinical applicability and benefit is yet to be determined. KW - Machine Learning KW - Computational Diagnosis KW - Knee Osteoarthritis Y1 - 2021 ER - TY - JOUR A1 - Müller, Sebastian A1 - Paltra, Sydney A1 - Rehmann, Jakob A1 - Nagel, Kai A1 - Conrad, Tim T1 - Explicit modeling of antibody levels for infectious disease simulations in the context of SARS-CoV-2 JF - iScience N2 - Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These antibody levels are dynamic: due to waning, antibody levels will drop over time. During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to support the planning of public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects. This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. The model was developed in response to the complexity of different immunization sequences and types and is based on neutralization titer studies. This approach allows complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases. Y1 - 2023 U6 - https://doi.org/10.1016/j.isci.2023.107554 VL - 26 IS - 9 ER - TY - CHAP A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks T2 - Computing in Cardiology (CinC) 2023 N2 - Aims: Interpretation of continuous EEG is a demanding task that requires the expertise of trained neurologists. However, these experts are not always available in many medical centers. As part of the 2023 George B. Moody PhysioNet Challenge, we developed a deep learning based method for analyzing EEG data of comatose patients and predicting prognosis following cardiac arrest. Methods: Our approach is a two-step pipeline that consists of a prediction model and a decision-making strategy. The prediction model is a residual neural network (ResNet-18) that extracts features and makes a prediction based on a short 5-minute EEG recording. In the second step, a majority vote over multiple predictions made for several EEG recordings of a patient determines the final prognosis. Results: Based on 10-fold cross-validation on the training set, we achieved a true positive rate (TPR) of 0.41 for predicting poor outcome while keeping the false positive rate below 0.05 at 72 hours after recovery of spontaneous circulation. On the official challenge leaderboard, our team ZIB_Visual scored 0.426 TPR. Conclusion: Our approach, while simple to implement and execute, faced overfitting challenges during the official competition phase. In this paper, we discuss our implementation and potential improvements to address these issues. Y1 - 2023 U6 - https://doi.org/10.22489/CinC.2023.093 VL - 50 PB - IEEE ER - TY - JOUR A1 - Bleich, Amnon A1 - Linnemann, Antje A1 - Jaidi, Benjamin A1 - Diem, Bjoern H A1 - Conrad, Tim T1 - Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification JF - Machine Learning and Knowledge Extraction N2 - Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient's heart rhythm and when triggered - send it to a secure server where health care professionals (denote HCPs from here on) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to alert for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in a relatively high false-positive rate) and this, combined with the device's nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing amount of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics that make its analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. It does so by combining high-frequency noise detection (which often occurs in ICM data) with a semi-supervised learning pipeline that allows for re-labeling of training episodes, and by using segmentation and dimension reduction techniques that are robust to morphology variations of the sECG signal (which are typical to ICM data). As a result, it performs better than state-of-the-art techniques on such data with e.g. F1 score of 0.51 vs. 0.38 of our baseline state-of-the-art technique in correctly calling Atrial Fibrilation in ICM data. As such, it could be used in numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs by, e.g., suggesting a rhythm type. Y1 - 2023 U6 - https://doi.org/10.3390/make5040077 VL - 5 IS - 4 PB - MDPI ER - TY - JOUR A1 - Anteghini, Marco A1 - Martins dos Santos, Vitor AP A1 - Saccenti, Edoardo T1 - PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates JF - Journal of Cellular Biochemistry N2 - The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task. Y1 - 2023 U6 - https://doi.org/10.1002/jcb.30490 VL - 124 IS - 11 SP - 1665 EP - 1885 ER - TY - CHAP A1 - Anteghini, Marco A1 - Martins Dos Santos, Vitor T1 - Computational Approaches for Peroxisomal Protein Localization T2 - Peroxisomes N2 - Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization. Y1 - 2023 SN - 978-1-0716-3047-1 U6 - https://doi.org/10.1007/978-1-0716-3048-8_29 VL - 2643 SP - 405 EP - 411 PB - Humana, New York ER - TY - CHAP A1 - Schubotz, Moritz A1 - Ferrer, Eloi A1 - Stegmüller, Johannes A1 - Mietchen, Daniel A1 - Teschke, Olaf A1 - Pusch, Larissa A1 - Conrad, Tim T1 - Bravo MaRDI: A Wikibase Knowledge Graph on Mathematics T2 - Proceedings of the 4th Wikidata Workshop 2022 co-located with the 22st International Semantic Web Conference (ISWC2023) N2 - Mathematical world knowledge is a fundamental component of Wikidata. However, to date, no expertly curated knowledge graph has focused specifically on contemporary mathematics. Addressing this gap, the Mathematical Research Data Initiative (MaRDI) has developed a comprehensive knowledge graph that links multimodal research data in mathematics. This encompasses traditional research data items like datasets, software, and publications and includes semantically advanced objects such as mathematical formulae and hypotheses. This paper details the abilities of the MaRDI knowledge graph, which is based on Wikibase, leading up to its inaugural public release, codenamed Bravo, available on https://portal.mardi4nfdi.de. Y1 - 2023 ER - TY - JOUR A1 - Sengupta, Agniva A1 - Bartoli, Adrien T1 - Convex Solutions to SfT and NRSfM under Algebraic Deformation Models JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods. Y1 - 2025 U6 - https://doi.org/10.1109/TPAMI.2025.3635039 ER - TY - CHAP A1 - Manogue, Kevin A1 - Schang, Tomasz A1 - Kuş, Dilara A1 - Müller, Jonas A1 - Zachow, Stefan A1 - Sengupta, Agniva T1 - Generalizing Shape-from-Template to Topological Changes T2 - Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference N2 - Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. Y1 - 2025 SN - 978-3-03868-296-7 U6 - https://doi.org/10.2312/stag.20251322 PB - The Eurographics Association ER - TY - JOUR A1 - Pusch, Larissa A1 - Conrad, Tim T1 - Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Biomedical Question Answering JF - BioMedInformatics N2 - Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models generate infor- mation not grounded in data—can lead to dangerous misinformation. This paper presents a hybrid approach that combines LLMs with Knowledge Graphs (KGs) to improve the accuracy and reliability of question-answering systems in the biomedical field. Our method, implemented using the LangChain framework, includes a query-checking algorithm that checks and, where possible, corrects LLM-generated Cypher queries, which are then exe- cuted on the Knowledge Graph, grounding answers in the KG and reducing hallucinations in the evaluated cases. We evaluated several LLMs, including several GPT models and Llama 3.3:70b, on a custom benchmark dataset of 50 biomedical questions. GPT-4 Turbo achieved 90% query accuracy, outperforming most other models. We also evaluated prompt engineering, but found little statistically significant improvement compared to the standard prompt, except for Llama 3:70b, which improved with few-shot prompting. To enhance usability, we developed a web-based interface that allows users to input natural language queries, view generated and corrected Cypher queries, and inspect results for accuracy. This framework improves reliability and accessibility by accepting natural language questions and returning verifiable answers directly from the knowledge graph, enabling inspection and reproducibility. The source code for generating the results of this paper and for the user- interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui, accessed on 1 November 2025. Y1 - 2025 U6 - https://doi.org/10.3390/biomedinformatics5040070 VL - 5 ER - TY - JOUR A1 - Pfeuffer, Julianus A1 - Bielow, Chris A1 - Wein, Samuel A1 - Jeong, Kyowon A1 - Netz, Eugen A1 - Walter, Axel A1 - Alka, Oliver A1 - Nilse, Lars A1 - Colaianni, Pasquale Domenico A1 - McCloskey, Douglas A1 - Kim, Jihyung A1 - Rosenberger, George A1 - Bichmann, Leon A1 - Walzer, Mathias A1 - Veit, Johannes A1 - Boudaud, Bertrand A1 - Bernt, Matthias A1 - Patikas, Nikolaos A1 - Pilz, Matteo A1 - Startek, Michał Piotr A1 - Kutuzova, Svetlana A1 - Heumos, Lukas A1 - Charkow, Joshua A1 - Sing, Justin Cyril A1 - Feroz, Ayesha A1 - Siraj, Arslan A1 - Weisser, Hendrik A1 - Dijkstra, Tjeerd M. H. A1 - Perez-Riverol, Yasset A1 - Röst, Hannes A1 - Kohlbacher, Oliver A1 - Sachsenberg, Timo T1 - OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data JF - Nature Methods Y1 - 2024 U6 - https://doi.org/10.1038/s41592-024-02197-7 SN - 1548-7091 VL - 21 IS - 3 SP - 365 EP - 367 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification JF - IEEE Journal of Biomedical and Health Informatics Y1 - 2024 U6 - https://doi.org/10.1109/JBHI.2024.3427787 VL - 28 IS - 11 ER - TY - GEN A1 - Hajarolasvadi, Noushin A1 - Baum, Daniel T1 - Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches N2 - A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled ”DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography” paper. Y1 - 2024 U6 - https://doi.org/10.12752/9686 ER - TY - JOUR A1 - Maier, Kristina A1 - Weiser, Martin A1 - Conrad, Tim T1 - Hybrid PDE-ODE Models for Efficient Simulation of Infection Spread in Epidemiology JF - Proceedings of the Royal Society A N2 - This paper introduces a novel hybrid model combining Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) to simulate infectious disease dynamics across geographic regions. By leveraging the spatial detail of PDEs and the computational efficiency of ODEs, the model enables rapid evaluation of public health interventions. Applied to synthetic environments and real-world scenarios in Lombardy, Italy, and Berlin, Germany, the model highlights how interactions between PDE and ODE regions affect infection dynamics, especially in high-density areas. Key findings reveal that the placement of model boundaries in densely populated regions can lead to inaccuracies in infection spread, suggesting that boundaries should be positioned in areas of lower population density to better reflect transmission dynamics. Additionally, regions with low population density hinder infection flow, indicating a need for incorporating, e.g., jumps in the model to enhance its predictive capabilities. Results indicate that the hybrid model achieves a balance between computational speed and accuracy, making it a valuable tool for policymakers in real-time decision-making and scenario analysis in epidemiology and potentially in other fields requiring similar modeling approaches. Y1 - 2025 U6 - https://doi.org/10.1098/rspa.2024.0421 VL - 481 IS - 2306 PB - Royal Society ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems JF - International Journal of Data Science and Analytics Y1 - 2024 U6 - https://doi.org/10.1007/s41060-024-00635-5 IS - 2024 PB - Springer Nature ER - TY - JOUR A1 - Anteghini, Marco A1 - Gualdi, Francesco A1 - Oliva, Baldo T1 - How did we get there? AI applications to biological networks and sequences JF - Computers in Biology and Medicine N2 - The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein–protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field. Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2025.110064 SN - 0010-4825 VL - 190 PB - Elsevier BV ER - TY - JOUR A1 - Sengupta, Agniva A1 - Zachow, Stefan T1 - Shape-from-Template with Generalised Camera JF - Image and Vision Computing N2 - This article presents a new method for non-rigidly registering a 3D shape to 2D keypoints observed by a constellation of multiple cameras. Non-rigid registration of a 3D shape to observed 2D keypoints, i.e., Shape-from-Template (SfT), has been widely studied using single images, but SfT with information from multiple-cameras jointly opens new directions for extending the scope of known use-cases such as 3D shape registration in medical imaging and registration from hand-held cameras, to name a few. We represent such multi-camera setup with the generalised camera model; therefore any collection of perspective or orthographic cameras observing any deforming object can be registered. We propose multiple approaches for such SfT: the first approach where the corresponded keypoints lie on a direction vector from a known 3D point in space, the second approach where the corresponded keypoints lie on a direction vector from an unknown 3D point in space but with known orientation w.r.t some local reference frame, and a third approach where, apart from correspondences, the silhouette of the imaged object is also known. Together, these form the first set of solutions to the SfT problem with generalised cameras. The key idea behind SfT with generalised camera is the improved reconstruction accuracy from estimating deformed shape while utilising the additional information from the mutual constraints between multiple views of a deformed object. The correspondence-based approaches are solved with convex programming while the silhouette-based approach is an iterative refinement of the results from the convex solutions. We demonstrate the accuracy of our proposed methods on many synthetic and real data. Y1 - 2025 U6 - https://doi.org/10.1016/j.imavis.2025.105579 VL - 162 ER - TY - THES A1 - Bautz, Lisa T1 - Unsupervised Shape Correspondence Estimation for Anatomical Shapes N2 - The concept of shape correspondence describes a relation between two or more shapes of the same class. It often consists of a mapping between points on semantically similar locations of all shapes. One possible application for shape correspondence in medicine is the automatic location of anatomical landmarks. Another popular application is the construction of statistical shape models. These models are an established way to represent geometric variation of anatomical shapes in a compact way. Possible applications range from the generation of shapes and reconstruction tasks to disease classification. This thesis aims to investigate unsupervised methods that can be used to estimate such a correspondence on anatomical shapes. While most methods used in the medical domain focus on classical optimization algorithms to establish correspondence, the broader computer vision domain developed a versatile field of data-driven methods. Recently, the new shape model FlowSSM was introduced, which does not require predefined correspondences for training as it generates them itself. As the performance of the shape model is quite competitive, it is natural to assume that the generated correspondences are of high quality as well. For this reason, we evaluate the quality of the correspondences generated by FlowSSM within this thesis. Furthermore, we modify the method by adding a second loss term that minimizes geodesic distortions. This is done to favor isometric deformations which can lead to better correspondences. We compare the results with two established methods from the medical domain, LDDMM and Meshmonk. Furthermore, we investigate the performance of a fourth method called Neuromoph. This data-driven method comes from the wider computer vision field and was not tested on anatomical data yet. All methods are evaluated with a set of different metrics. This includes metrics to assess the quality of the resulting meshes, a sparse correspondence error on anatomical landmarks, and metrics to measure the quality of the resulting shape models. Furthermore, we test all methods on three datasets with different degrees of geometric variation, namely liver, distal femur and face. We show that FlowSSM produces correspondences with state-of-the-art quality. Moreover, our modification further improved the quality of correspondences at a global level. Nevertheless, there is no clear ranking between all methods, as the results differ between metrics and datasets. Thereby, we can show that there are different qualities to a proper correspondence which are reflected in the different metrics. It is therefore strongly recommendable to choose a correspondence estimation method specifically for the problem at hand. N2 - Das Konzept der Formkorrespondenz zwischen 3D-Objekten einer Klasse beschreibt eine Beziehung zwischen den Instanzen (oft Punkten) der unterschiedlichen Objekten. Hierbei werden Punkte, die an semantisch gleichwertigen Orten liegen, miteinander in Verbindung gebracht. Eine mögliche Anwednung der Formkorrespondenz im medizinischen Bereich ist daher die automatisierte Lokalisierung von anatomischen Landmarken. Eine weitere Anwendung ist das Erstellen von statistischen Formmodellen. Mit diesen kann die geometrische Variation anatomischer Formen kompakt abgebildet werden. Medizinische Anwendungen reichen dabei von der einfachen Formgenerierung zu komplexeren Rekonstruktionsaufgaben und der Klassifizierung von gesunden und pathologischen Formen. In dieser Arbeit werden unterschiedliche Methoden zur Erzeugung von Formkorrespondenzen untersucht. Die entsprechende Literatur im medizinischen Bereich verwendet hierzu meist Methoden, die das klassische Optimierungsproblem einer nichtrigiden Transformation lösen. Im Computer Vision Bereich wurden in den letzten Jahren auch einige datengetriebene Methoden zur Korrespondenzgenerierung veröffentlicht. Im letzten Jahr wurde außerdem die Methode FlowSSM zur Erstellung statistischer Formmodelle vorgestellt, die nicht auf korrespondierenden Oberflächen basiert, sondern diese selbst erzeugt. Da FlowSSM trotzdem konkurenzfähige Ergebnisse erzielt, ist naheliegend, dass auch die zugrundeliegenden, selbst generierten Korrespondenzen von hoher Qualität sind. Innerhalb dieser Arbeit wird daher die Qualität der von FlowSSM erzeugten Korrespondenzen evaluiert. Außerdem wird die Methode um eine zusätzliche Kostenfunktion erweitert, die geod#tische Verzerrungen verhindern soll. Dadurch sollen nichtisometrische Deformationen vermieden werden, wodurch die Qualität der resultierenden Korrenspondenzen gesteigert werden kann. Die Ergebnisse von FlowSSM werden mit zwei etablierten Methoden aus dem medizinischen Bereich, LDDMM und Meshmonk, verglichen. Außerdem wird NeuroMorph, eine aktuelle, datengetriebene Methode aus dem Bereich des maschinellen Sehens getestet. Letztere wurde bisher noch nicht auf medizinischen Daten evaluiert. Die Bewertung aller generierten Korrespondenzen basiert auf ausgewählten indirekten Metriken. Hierzu gehört auch die Performance bei konkreten Anwendungsfällen wie der Lokalisierung von Landmarken und dem Erstellen von statistischen Formmodellen. Im Rahmen der Arbeit wird gezeigt, dass FlowSSM Korrespondenzen produziert, deren Qualität dem aktuellen State-of-the-art entspricht. Durch das Hinzufügen der zweiten Kostenfunktion wird die Qualität der Korrespondenzen auf einem globalen Level noch weiter gesteigert. Prinzipiell lässt sich jedoch keine Hierarchie zwischen den Methoden ableiten, da die Performance stark innerhalb der untersuchten Metriken und Datensätzen schwankt. Die Auswahl einer passenden Methode sollte sich daher vor allem am Anwendungsfall orientieren. Y1 - 2023 ER - TY - JOUR A1 - Fogalli, Giovani Bressan A1 - Peres Line, Sérgio Roberto A1 - Baum, Daniel T1 - Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches JF - Frontiers in Imaging N2 - Tooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter-Schreger Bands (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual‘s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward. We used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a fully convolutional neural network. Both approaches were applied to a set of extracted tooth images. U-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766. Even with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties. Y1 - 2023 U6 - https://doi.org/10.3389/fimag.2023.1215764 VL - 2 ER - TY - JOUR A1 - Bai, Mingze A1 - Deng, Jingwen A1 - Dai, Chengxin A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Perez-Riverol, Yasset T1 - LFQ-Based Peptide and Protein Intensity Differential Expression Analysis JF - J. Proteome Res. N2 - Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings. Y1 - 2023 U6 - https://doi.org/10.1021/acs.jproteome.2c00812 VL - 22 IS - 6 SP - 2114 EP - 2123 PB - American Chemical Society ER - TY - JOUR A1 - Kontou, Eftychia E. A1 - Walter, Axel A1 - Alka, Oliver A1 - Pfeuffer, Julianus A1 - Sachsenberg, Timo A1 - Mohite, Omkar A1 - Nuhamunanda, Matin A1 - Kohlbacher, Oliver A1 - Weber, Tilmann T1 - UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis JF - Journal of Cheminformatics N2 - Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00724-w VL - 15 ER - TY - JOUR A1 - Paltra, Sydney A1 - Conrad, Tim T1 - Clinical Effectiveness of Ritonavir-Boosted Nirmatrelvir—A Literature Review JF - Advances in Respiratory Medicine N2 - Nirmatrelvir/Ritonavir is an oral treatment for mild to moderate COVID-19 cases with a high risk for a severe course of the disease. For this paper, a comprehensive literature review was performed, leading to a summary of currently available data on Nirmatrelvir/Ritonavir’s ability to reduce the risk of progressing to a severe disease state. Herein, the focus lies on publications that include comparisons between patients receiving Nirmatrelvir/Ritonavir and a control group. The findings can be summarized as follows: Data from the time when the Delta-variant was dominant show that Nirmatrelvir/Ritonavir reduced the risk of hospitalization or death by 88.9% for unvaccinated, non-hospitalized high-risk individuals. Data from the time when the Omicron variant was dominant found decreased relative risk reductions for various vaccination statuses: between 26% and 65% for hospitalization. The presented papers that differentiate between unvaccinated and vaccinated individuals agree that unvaccinated patients benefit more from treatment with Nirmatrelvir/Ritonavir. However, when it comes to the dependency of potential on age and comorbidities, further studies are necessary. From the available data, one can conclude that Nirmatrelvir/Ritonavir cannot substitute vaccinations; however, its low manufacturing cost and easy administration make it a valuable tool in fighting COVID-19, especially for countries with low vaccination rates. Y1 - 2024 U6 - https://doi.org/10.3390/arm92010009 VL - 92 IS - 1 ER - TY - THES A1 - Pfeuffer, Julianus T1 - Computational Methods for Protein Inference in Shotgun Proteomics Experiments N2 - Since the beginning of this millennium, the advent of high-throughput methods in numerous fields of the life sciences led to a shift in paradigms. A broad variety of technologies emerged that allow comprehensive quantification of molecules involved in biological processes. Simultaneously, a major increase in data volume has been recorded with these techniques through enhanced instrumentation and other technical advances. By supplying computational methods that automatically process raw data to obtain biological information, the field of bioinformatics plays an increasingly important role in the analysis of the ever-growing mass of data. Computational mass spectrometry in particular, is a bioinformatics field of research which provides means to gather, analyze and visualize data from high-throughput mass spectrometric experiments. For the study of the entirety of proteins in a cell or an environmental sample, even current techniques reach limitations that need to be circumvented by simplifying the samples subjected to the mass spectrometer. These pre-digested (so-called bottom-up) proteomics experiments then pose an even bigger computational burden during analysis since complex ambiguities need to be resolved during protein inference, grouping and quantification. In this thesis, we present several developments in the pursuit of our goal to provide means for a fully automated analysis of complex and large-scale bottom-up proteomics experiments. Firstly, due to prohibitive computational complexities in state-of-the-art Bayesian protein inference techniques, a refined, more stable technique for performing inference on sums of random variables was developed to enable a variation of standard Bayesian inference for the problem. nextflow and part of a set of standardized, well-tested, and community-maintained workflows by the nf-core collective. Our workflow runs on large-scale data with complex experimental designs and allows a one-command analysis of local and publicly available data sets with state-of-the-art accuracy on various high-performance computing environments or the cloud. Y1 - 2023 UR - http://hdl.handle.net/10900/142644 ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance JF - Computers in Biology and Medicine N2 - Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated datasets, which are difficult and costly to collect. To address this issue, transfer learning is often employed, where models are pre-trained on large datasets and fine-tuned for specific ECG classification tasks with limited labeled data. Self-supervised learning has become a widely adopted pre-training method, enabling models to learn meaningful representations from unlabeled datasets. In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data. Unlike invariance-based methods, JEPA does not rely on hand-crafted data augmentations, and unlike generative methods, it predicts latent features rather than reconstructing input data. We create a large unsupervised pre-training dataset by combining ten public ECG databases, amounting to over one million records. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our results show that JEPA outperforms existing invariance-based and generative approaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA consistently learns the highest quality representations, as demonstrated in frozen evaluations, and proves advantageous for pre-training even in the absence of additional data. Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2025.110809 SN - 0010-4825 VL - 196 PB - Elsevier BV ER - TY - JOUR A1 - Bartoli, Adrien A1 - Sengupta, Agniva T1 - Camera Pose in SfT and NRSfM under Isometric and Weaker Deformation Models JF - Computer Vision and Image Understanding N2 - Camera pose is a very natural concept in 3D vision in the rigid setting. It is however much more difficult to work with in deformable settings. Consequently, numerous deformable reconstruction methods simply ignore camera pose. We analyse the concept of pose in deformable settings and prove that it is unconstrained with the existing formulations, properly justifying the existing pose-less methods reconstructing structure only. We explain this result intuitively by the impossibility to define an intrinsic coordinate frame to a general deforming object. The proposed analysis uses the isometric deformation model and extends to the weaker models including conformality and equiareality. We propose a novel prior to rescue camera pose estimation in deformable settings, which attributes the deforming object’s dominant rigid-body motion to the camera. We show that adding this prior to any existing formulation fully constrains camera pose and leads to elegant two-step solution methods, involving deformable structure reconstruction using a base method in the first step, and absolute orientation or Procrustes analysis in the second step. We derive the proposed approach for the template-based and template-less settings, respectively implemented using Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) as base methods, and validate them experimentally, showing that the computed pose is qualitatively and quantitatively plausible. Y1 - 2025 U6 - https://doi.org/10.1016/j.cviu.2025.104488 VL - 261 ER - TY - THES A1 - Amiranashvili, Tamaz T1 - Universal and Expressive Statistical Shape Models for Anatomical Structures N2 - Form and function of anatomical structures are intimately linked. Pathological changes in form can be associated with the loss of function. For example, diseases often cause characteristic shape changes, making shape a sensitive structural biomarker for medical diagnosis. If the link between form and function is causal, correcting a pathological shape can even restore the healthy function of an organ. Accurate shape reconstruction is then crucial for effective, patient-specific treatment planning. This demonstrates the importance of shape in clinical interventions and its potential to improve overall patient outcomes. Statistical shape models are computational methods that capture shape variations in a given population and enable precise shape analysis and generation. We focus on two key properties of a good statistical shape model. First, it should be easy to construct, and second, it should accurately represent the underlying shape distribution. Established existing approaches can only be constructed from surfaces with pre-defined dense correspondence. Such correspondence is tedious to obtain, can introduce undesired biases, and prevents training on partial or sparse observations. While correspondence-free methods exist, they struggle to accurately capture shape distributions with intricate details and large variations. In this thesis, we develop shape models that simplify training and improve accuracy over state-of-the-art. To achieve these goals, we build on approximately diffeomorphic neural deformations and implicit neural representations. First, our proposed methods are trainable on correspondence-free surfaces and even partial segmentations with large slice distances. This makes them universal since they can be trained on heterogeneous data, enabling scalability to large datasets and avoiding potential biases of pre-defined correspondence. Second, our methods are highly expressive, accurately capturing intricate shape details in complex distributions. We evaluate effectiveness of our models on multiple anatomical structures, outperforming established baselines in both generative and discriminative settings. Y1 - 2025 UR - https://mediatum.ub.tum.de/?id=1776778 UR - https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20250717-1776778-0-4 ER - TY - JOUR A1 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Husseini, Malek E. A1 - Bayat, Amirhossein A1 - Löffler, Maximilian A1 - Liebl, Hans A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Brown, Kevin A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Chen, Di A1 - Bai, Yiwei A1 - Rapazzo, Brandon H. A1 - Yeah, Timyoas A1 - Zhang, Amber A1 - Xu, Shangliang A1 - Hou, Feng A1 - He, Zhiqiang A1 - Zeng, Chan A1 - Xiangshang, Zheng A1 - Liming, Xu A1 - Netherton, Tucker J. A1 - Mumme, Raymond P. A1 - Court, Laurence E. A1 - Huang, Zixun A1 - He, Chenhang A1 - Wang, Li-Wen A1 - Ling, Sai Ho A1 - Huynh, Lê Duy A1 - Boutry, Nicolas A1 - Jakubicek, Roman A1 - Chmelik, Jiri A1 - Mulay, Supriti A1 - Sivaprakasam, Mohanasankar A1 - Paetzold, Johannes C. A1 - Shit, Suprosanna A1 - Ezhov, Ivan A1 - Wiestler, Benedikt A1 - Glocker, Ben A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images JF - Medical Image Analysis N2 - Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102166 VL - 73 ER - TY - THES A1 - Punjabi, Dev T1 - Orientation-invariant Dense Correspondence using Graph Convolutional Neural Networks Y1 - 2021 ER - TY - JOUR A1 - Melnyk, Kateryna A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Understanding microbiome dynamics via interpretable graph representation learning JF - Scientific Reports N2 - Large-scale perturbations in the microbiome constitution are strongly correlated, whether as a driver or a consequence, with the health and functioning of human physiology. However, understanding the difference in the microbiome profiles of healthy and ill individuals can be complicated due to the large number of complex interactions among microbes. We propose to model these interactions as a time-evolving graph whose nodes are microbes and edges are interactions among them. Motivated by the need to analyse such complex interactions, we develop a method that learns a low-dimensional representation of the time-evolving graph and maintains the dynamics occurring in the high-dimensional space. Through our experiments, we show that we can extract graph features such as clusters of nodes or edges that have the highest impact on the model to learn the low-dimensional representation. This information can be crucial to identify microbes and interactions among them that are strongly correlated with clinical diseases. We conduct our experiments on both synthetic and real-world microbiome datasets. Y1 - 2023 U6 - https://doi.org/10.1038/s41598-023-29098-7 VL - 13 SP - 2058 ER - TY - THES A1 - Şirin, Ege T1 - Probabilistic Image Segmentation With Continuous Shape Representations Y1 - 2023 ER - TY - JOUR A1 - Secker, Christopher A1 - Fackeldey, Konstantin A1 - Weber, Marcus A1 - Ray, Sourav A1 - Gorgulla, Christoph A1 - Schütte, Christof T1 - Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists JF - Journal of Cheminformatics N2 - Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the μ-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported β-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale. Y1 - 2023 U6 - https://doi.org/10.1186/s13321-023-00746-4 VL - 15 ER -