TY - JOUR A1 - Conrad, Tim A1 - Ferrer, Eloi A1 - Mietchen, Daniel A1 - Pusch, Larissa A1 - Stegmuller, Johannes A1 - Schubotz, Moritz T1 - Making Mathematical Research Data FAIR: Pathways to Improved Data Sharing JF - Scientific Data Y1 - 2024 U6 - https://doi.org/10.1038/s41597-024-03480-0 VL - 11 ER - TY - JOUR A1 - Fürst, Steffen A1 - Conrad, Tim A1 - Jaeger, Carlo A1 - Wolf, Sarah T1 - Vahana.jl - A framework (not only) for large-scale agent-based models JF - Proceedings of Social Simulation Conference 2024 (SSC24) Y1 - 2024 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 - 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 - Bostanci, Inan A1 - Conrad, Tim T1 - Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach JF - Journal of Artificial Societies and Social Simulation N2 - This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at: git.zib.de/ibostanc/hybrid_abm_ode. Y1 - 2025 U6 - https://doi.org/10.18564/jasss.5567 VL - 28 IS - 1 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 - 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 - CHAP A1 - Maignant, Elodie A1 - Conrad, Tim A1 - von Tycowicz, Christoph T1 - Tree inference with varifold distances T2 - Geometric Science of Information. GSI 2025 N2 - In this paper, we consider a tree inference problem motivated by the critical problem in single-cell genomics of reconstructing dynamic cellular processes from sequencing data. In particular, given a population of cells sampled from such a process, we are interested in the problem of ordering the cells according to their progression in the process. This is known as trajectory inference. If the process is differentiation, this amounts to reconstructing the corresponding differentiation tree. One way of doing this in practice is to estimate the shortest-path distance between nodes based on cell similarities observed in sequencing data. Recent sequencing techniques make it possible to measure two types of data: gene expression levels, and RNA velocity, a vector that predicts changes in gene expression. The data then consist of a discrete vector field on a (subset of a) Euclidean space of dimension equal to the number of genes under consideration. By integrating this velocity field, we trace the evolution of gene expression levels in each single cell from some initial stage to its current stage. Eventually, we assume that we have a faithful embedding of the differentiation tree in a Euclidean space, but which we only observe through the curves representing the paths from the root to the nodes. Using varifold distances between such curves, we define a similarity measure between nodes which we prove approximates the shortest-path distance in a tree that is isomorphic to the target tree. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-03921-7_30 VL - 16034 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 - JOUR A1 - Liang, YongTian A1 - Piao, Chengji A1 - Beuschel, Christine B. A1 - Toppe, David A1 - Kollipara, Laxmikanth A1 - Bogdanow, Boris A1 - Maglione, Marta A1 - Lützkendorf, Janine A1 - See, Jason Chun Kit A1 - Huang, Sheng A1 - Conrad, Tim A1 - Kintscher, Ulrich A1 - Madeo, Frank A1 - Liu, Fan A1 - Sickmann, Albert A1 - Sigrist, Stephan J. T1 - eIF5A hypusination, boosted by dietary spermidine, protects from premature brain aging and mitochondrial dysfunction JF - Cell Reports Y1 - 2021 U6 - https://doi.org/10.1016/j.celrep.2021.108941 VL - 35 IS - 2 ER - TY - JOUR A1 - Melnyk, Kateryna A1 - Montavon, Grègoire A1 - Klus, Stefan A1 - Conrad, Tim T1 - Graph Kernel Koopman Embedding for Human Microbiome Analysis JF - Applied Network Science N2 - More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes. Y1 - 2020 U6 - https://doi.org/10.1007/s41109-020-00339-2 VL - 5 IS - 96 ER - TY - JOUR A1 - Iravani, Sahar A1 - Conrad, Tim T1 - An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics N2 - Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Y1 - 2023 U6 - https://doi.org/10.1109/tcbb.2022.3141656 VL - 20 IS - 1 SP - 151 EP - 161 ER - TY - JOUR A1 - Rams, Mona A1 - Conrad, Tim T1 - Dictionary learning allows model-free pseudotime estimation of transcriptomics data JF - BMC Genomics Y1 - 2022 U6 - https://doi.org/10.1186/s12864-021-08276-9 VL - 23 PB - BioMed Central ER - TY - JOUR A1 - Weimann, K. A1 - Conrad, Tim T1 - Transfer Learning for ECG Classification JF - Scientific Reports N2 - Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to 6.57%, effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-84374-8 VL - 11 ER - TY - JOUR A1 - Le Duc, Huy A1 - Conrad, Tim T1 - A light-weight and highly flexible software system for analyzing large bio-medical datasets JF - Future Generation Computer Systems Y1 - 2020 ER - TY - JOUR A1 - Juds, Carmen A1 - Schmidt, Johannes A1 - Weller, Michael A1 - Lange, Thorid A1 - Conrad, Tim A1 - Boerner, Hans T1 - Combining Phage Display and Next-generation Sequencing for Materials Sciences: A Case Study on Probing Polypropylene Surfaces JF - Journal of the American Chemical Society N2 - Phage display biopanning with Illumina next-generation sequencing (NGS) is applied to reveal insights into peptide-based adhesion domains for polypropylene (PP). One biopanning round followed by NGS selects robust PP-binding peptides that are not evident by Sanger sequencing. NGS provides a significant statistical base that enables motif analysis, statistics on positional residue depletion/enrichment, and data analysis to suppress false-positive sequences from amplification bias. The selected sequences are employed as water-based primers for PP?metal adhesion to condition PP surfaces and increase adhesive strength by 100\% relative to nonprimed PP. Y1 - 2020 U6 - https://doi.org/10.1021/jacs.0c03482 VL - 142 IS - 24 SP - 10624 EP - 10628 ER - TY - JOUR A1 - Cvetkovic, Nada A1 - Conrad, Tim A1 - Lie, Han Cheng T1 - A Convergent Discretisation Method for Transition Path Theory for Diffusion Processes JF - Multiscale Modeling & Simulation Y1 - 2021 U6 - https://doi.org/10.1137/20M1329354 VL - 19 IS - 1 SP - 242 EP - 266 PB - Society for Industrial and Applied Mathematics ER - TY - JOUR A1 - Tuncay, Erhun Giray A1 - Erdur, Rıza Cenk A1 - Conrad, Tim T1 - Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics N2 - The aim of Network Alignment in Protein-Protein Interaction Networks is discovering functionally similar regions between compared organisms. One major compromise for solving a network alignment problem is the trade-off among multiple similarity objectives while applying an alignment strategy. An alignment may lose its biological relevance while favoring certain objectives upon others due to the actual relevance of unfavored objectives. One possible solution for solving this issue may be blending the stronger aspects of various alignment strategies until achieving mature solutions. This study proposes a parallel approach called PERSONA that allows aligners to share their partial solutions continuously while they progress. All these aligners pursue their particular heuristics as part of a particle swarm that searches for multi-objective solutions of the same alignment problem in a reactive actor environment. The actors use the stronger portion of a solution as a subgraph that they receive from leading or other actors and send their own stronger subgraphs back upon evaluation of those partial solutions. Moreover, the individual heuristics of each actor takes randomized parameter values at each cycle of parallel execution so that the problem search space can thoroughly be investigated. The results achieved with PERSONA are remarkably optimized and balanced for both topological and node similarity objectives. Y1 - 2023 U6 - https://doi.org/10.1109/TCBB.2022.3231489 VL - 20 IS - 3 SP - 2064 EP - 2077 ER - TY - JOUR A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Obermeier, Patrick A1 - Ma, Xiaolin A1 - Schweiger, Brunhilde A1 - Opota, Onya A1 - Rath, Barbara T1 - Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study JF - Viruses N2 - Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0–18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU (‘non-ICU’) versus management requiring ICU care (‘ICU’) added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8% of influenza patients (non-ICU) and 1.5% of ICU patients with influenza received antivirals; only 2.9% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction. Y1 - 2024 U6 - https://doi.org/10.3390/v16040507 VL - 16 IS - 4 ER - TY - JOUR A1 - Sherratt, Katharine A1 - Srivastava, Ajitesh A1 - Ainslie, Kylie A1 - Singh, David E. A1 - Cublier, Aymar A1 - Marinescu, Maria Cristina A1 - Carretero, Jesus A1 - Garcia, Alberto Cascajo A1 - Franco, Nicolas A1 - Willem, Lander A1 - Abrams, Steven A1 - Faes, Christel A1 - Beutels, Philippe A1 - Hens, Niel A1 - Müller, Sebastian A1 - Charlton, Billy A1 - Ewert, Ricardo A1 - Paltra, Sydney A1 - Rakow, Christian A1 - Rehmann, Jakob A1 - Conrad, Tim A1 - Schütte, Christof A1 - Nagel, Kai A1 - Abbott, Sam A1 - Grah, Rok A1 - Niehus, Rene A1 - Prasse, Bastian A1 - Sandmann, Frank A1 - Funk, Sebastian T1 - Characterising information gains and losses when collecting multiple epidemic model outputs JF - Epidemics N2 - Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results. By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions. We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss KW - Virology KW - Infectious Diseases KW - Public Health, Environmental and Occupational Health KW - Microbiology KW - Parasitology KW - Epidemiology Y1 - 2024 U6 - https://doi.org/10.1016/j.epidem.2024.100765 SN - 1755-4365 VL - 47 PB - Elsevier BV ER - TY - JOUR A1 - Gaskin, Thomas A1 - Conrad, Tim A1 - Pavliotis, Grigorios A. A1 - Schütte, Christof T1 - Neural parameter calibration and uncertainty quantification for epidemic forecasting JF - PLOS ONE N2 - The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available. Y1 - 2024 U6 - https://doi.org/10.1371/journal.pone.0306704 VL - 19 IS - 10 ER - TY - JOUR A1 - Obermeier, Patrick E A1 - Heim, Albert A1 - Biere, Barbara A1 - Hage, Elias A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Schweiger, Brunhilde A1 - Rath, Barbara A T1 - Linking digital surveillance and in-depth virology to study clinical patterns of viral respiratory infections in vulnerable patient populations JF - iScience N2 - To improve the identification and management of viral respiratory infections, we established a clinical and virologic surveillance program for pediatric patients fulfilling pre-defined case criteria of influenza-like illness and viral respiratory infections. The program resulted in a cohort comprising 6,073 patients (56% male, median age 1.6 years, range 0–18.8 years), where every patient was assessed with a validated disease severity score at the point-of-care using the ViVI ScoreApp. We used machine learning and agnostic feature selection to identify characteristic clinical patterns. We tested all patients for human adenoviruses, 571 (9%) were positive. Adenovirus infections were particularly common and mild in children ≥1 month of age but rare and potentially severe in neonates: with lower airway involvement, disseminated disease, and a 50% mortality rate (n = 2/4). In one fatal case, we discovered a novel virus … Y1 - 2022 U6 - https://doi.org/10.1016/j.isci.2022.104276 VL - 25 IS - 5 PB - Cell Press ER - TY - JOUR A1 - Zhang, Wei A1 - Klus, Stefan A1 - Conrad, Tim A1 - Schütte, Christof T1 - Learning chemical reaction networks from trajectory data JF - SIAM Journal on Applied Dynamical Systems (SIADS) N2 - We develop a data-driven method to learn chemical reaction networks from trajectory data. Modeling the reaction system as a continuous-time Markov chain and assuming the system is fully observed,our method learns the propensity functions of the system with predetermined basis functions by maximizing the likelihood function of the trajectory data under l^1 sparse regularization. We demonstrate our method with numerical examples using synthetic data and carry out an asymptotic analysis of the proposed learning procedure in the infinite-data limit. Y1 - 2019 U6 - https://doi.org/10.1137/19M1265880 VL - 18 IS - 4 SP - 2000 EP - 2046 ER - TY - CHAP A1 - Iravani, Sahar A1 - Conrad, Tim ED - Holzinger, A. ED - Kieseberg, P. ED - Tjoa, A. ED - Weippl, E. T1 - Deep Learning for Proteomics Data for Feature Selection and Classification T2 - Machine Learning and Knowledge Extraction. CD-MAKE 2019 Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-29726-8_19 VL - 11713 PB - Springer, Cham ER -