@article{MelnykMontavonKlusetal.2020, author = {Melnyk, Kateryna and Montavon, Gr{\`e}goire and Klus, Stefan and Conrad, Tim}, title = {Graph Kernel Koopman Embedding for Human Microbiome Analysis}, volume = {5}, journal = {Applied Network Science}, number = {96}, doi = {10.1007/s41109-020-00339-2}, year = {2020}, abstract = {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.}, language = {en} } @article{IravaniConrad2023, author = {Iravani, Sahar and Conrad, Tim}, title = {An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data}, volume = {20}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, number = {1}, doi = {10.1109/tcbb.2022.3141656}, pages = {151 -- 161}, year = {2023}, abstract = {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.}, language = {en} } @article{RamsConrad2022, author = {Rams, Mona and Conrad, Tim}, title = {Dictionary learning allows model-free pseudotime estimation of transcriptomics data}, volume = {23}, journal = {BMC Genomics}, publisher = {BioMed Central}, doi = {10.1186/s12864-021-08276-9}, year = {2022}, language = {en} } @article{WeimannConrad2021, author = {Weimann, K. and Conrad, Tim}, title = {Transfer Learning for ECG Classification}, volume = {11}, journal = {Scientific Reports}, doi = {10.1038/s41598-021-84374-8}, year = {2021}, abstract = {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.}, language = {en} } @article{LeDucConrad2020, author = {Le Duc, Huy and Conrad, Tim}, title = {A light-weight and highly flexible software system for analyzing large bio-medical datasets}, journal = {Future Generation Computer Systems}, year = {2020}, language = {en} } @article{JudsSchmidtWelleretal.2020, author = {Juds, Carmen and Schmidt, Johannes and Weller, Michael and Lange, Thorid and Conrad, Tim and Boerner, Hans}, title = {Combining Phage Display and Next-generation Sequencing for Materials Sciences: A Case Study on Probing Polypropylene Surfaces}, volume = {142}, journal = {Journal of the American Chemical Society}, number = {24}, doi = {10.1021/jacs.0c03482}, pages = {10624 -- 10628}, year = {2020}, abstract = {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.}, language = {en} } @article{CvetkovicConradLie2021, author = {Cvetkovic, Nada and Conrad, Tim and Lie, Han Cheng}, title = {A Convergent Discretisation Method for Transition Path Theory for Diffusion Processes}, volume = {19}, journal = {Multiscale Modeling \& Simulation}, number = {1}, publisher = {Society for Industrial and Applied Mathematics}, doi = {10.1137/20M1329354}, pages = {242 -- 266}, year = {2021}, language = {en} } @article{TuncayErdurConrad2023, author = {Tuncay, Erhun Giray and Erdur, R{\i}za Cenk and Conrad, Tim}, title = {Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA}, volume = {20}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, number = {3}, doi = {10.1109/TCBB.2022.3231489}, pages = {2064 -- 2077}, year = {2023}, abstract = {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.}, language = {de} } @article{AlchikhConradObermeieretal.2024, author = {Alchikh, Maren and Conrad, Tim and Obermeier, Patrick and Ma, Xiaolin and Schweiger, Brunhilde and Opota, Onya and Rath, Barbara}, title = {Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study}, volume = {16}, journal = {Viruses}, number = {4}, doi = {10.3390/v16040507}, year = {2024}, abstract = {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.}, language = {en} } @article{SherrattSrivastavaAinslieetal.2024, author = {Sherratt, Katharine and Srivastava, Ajitesh and Ainslie, Kylie and Singh, David E. and Cublier, Aymar and Marinescu, Maria Cristina and Carretero, Jesus and Garcia, Alberto Cascajo and Franco, Nicolas and Willem, Lander and Abrams, Steven and Faes, Christel and Beutels, Philippe and Hens, Niel and M{\"u}ller, Sebastian and Charlton, Billy and Ewert, Ricardo and Paltra, Sydney and Rakow, Christian and Rehmann, Jakob and Conrad, Tim and Sch{\"u}tte, Christof and Nagel, Kai and Abbott, Sam and Grah, Rok and Niehus, Rene and Prasse, Bastian and Sandmann, Frank and Funk, Sebastian}, title = {Characterising information gains and losses when collecting multiple epidemic model outputs}, volume = {47}, journal = {Epidemics}, publisher = {Elsevier BV}, issn = {1755-4365}, doi = {10.1016/j.epidem.2024.100765}, year = {2024}, abstract = {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}, language = {en} } @article{GaskinConradPavliotisetal.2024, author = {Gaskin, Thomas and Conrad, Tim and Pavliotis, Grigorios A. and Sch{\"u}tte, Christof}, title = {Neural parameter calibration and uncertainty quantification for epidemic forecasting}, volume = {19}, journal = {PLOS ONE}, number = {10}, arxiv = {http://arxiv.org/abs/2312.03147}, doi = {10.1371/journal.pone.0306704}, year = {2024}, abstract = {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.}, language = {en} } @article{ObermeierHeimBiereetal.2022, author = {Obermeier, Patrick E and Heim, Albert and Biere, Barbara and Hage, Elias and Alchikh, Maren and Conrad, Tim and Schweiger, Brunhilde and Rath, Barbara A}, title = {Linking digital surveillance and in-depth virology to study clinical patterns of viral respiratory infections in vulnerable patient populations}, volume = {25}, journal = {iScience}, number = {5}, publisher = {Cell Press}, doi = {10.1016/j.isci.2022.104276}, year = {2022}, abstract = {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 …}, language = {en} } @article{ZhangKlusConradetal.2019, author = {Zhang, Wei and Klus, Stefan and Conrad, Tim and Sch{\"u}tte, Christof}, title = {Learning chemical reaction networks from trajectory data}, volume = {18}, journal = {SIAM Journal on Applied Dynamical Systems (SIADS)}, number = {4}, arxiv = {http://arxiv.org/abs/1902.04920}, doi = {10.1137/19M1265880}, pages = {2000 -- 2046}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{IravaniConrad2019, author = {Iravani, Sahar and Conrad, Tim}, title = {Deep Learning for Proteomics Data for Feature Selection and Classification}, volume = {11713}, booktitle = {Machine Learning and Knowledge Extraction. CD-MAKE 2019}, editor = {Holzinger, A. and Kieseberg, P. and Tjoa, A. and Weippl, E.}, publisher = {Springer, Cham}, doi = {10.1007/978-3-030-29726-8_19}, year = {2019}, language = {en} }