TY - JOUR A1 - Sarich, Marco A1 - Djurdjevac Conrad, Natasa A1 - Bruckner, Sharon A1 - Conrad, Tim A1 - Schütte, Christof T1 - Modularity revisited: A novel dynamics-based concept for decomposing complex networks JF - Journal of Computational Dynamics Y1 - 2014 UR - http://publications.mi.fu-berlin.de/1127/ U6 - https://doi.org/10.3934/jcd.2014.1.191 VL - 1 IS - 1 SP - 191 EP - 212 ER - TY - JOUR A1 - Djurdjevac Conrad, Natasa A1 - Bruckner, Stefanie A1 - Conrad, Tim A1 - Schütte, Christof T1 - Random Walks on Complex Modular Networks JF - Journal of Numerical Analysis, Industrial and Applied Mathematics Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1090/ VL - 6 IS - 1-2 SP - 29 EP - 50 PB - European Society of Computational Methods in Sciences and Engineering 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 - Blaskovic, Filip A1 - Conrad, Tim A1 - Klus, Stefan A1 - Djurdjevac Conrad, Natasa T1 - Random walk based snapshot clustering for detecting community dynamics in temporal networks JF - Scientific Reports N2 - The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper, we introduce a novel random walk based approach that can identify clusters of time-snapshots in which network community structures are stable. This allows to detect significant structural shifts over time, such as the splitting, merging, birth, or death of communities. We also provide a low-dimensional representation of entire snapshots, placing those with similar community structure close to each other in the feature space. To validate our approach, we develop an agent-based algorithm that generates synthetic datasets with the desired characteristic properties, enabling thorough testing and benchmarking. We further demonstrate the effectiveness and broad applicability of our technique by testing it on various social dynamics models and real-world datasets and comparing its performance to several state-of-the-art algorithms. Our findings highlight the strength of our approach to correctly capture and analyze the dynamics of complex systems. Y1 - 2025 U6 - https://doi.org/10.1038/s41598-025-09340-0 VL - 15 SP - 24414 ER - TY - JOUR A1 - Borong, Shao A1 - Cannistraci, Carlo A1 - Conrad, Tim T1 - Epithelial Mesenchymal Transition Network-based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data JF - Genomics and Computational Biology N2 - Epithelial mesenchymal transition (EMT) process has been shown as highly relevant to cancer prognosis. However, although different biological network-based biomarker identification methods have been proposed to predict cancer prognosis, EMT network has not been directly used for this purpose. In this study, we constructed an EMT regulatory network consisting of 87 molecules and tried to select features that are useful for prognosis prediction in Lung Adenocarcinoma (LUAD). To incorporate multiple molecular profiles, we obtained four types of molecular data including mRNA-Seq, copy number alteration (CNA), DNA methylation, and miRNA-Seq data from The Cancer Genome Atlas. The data were mapped to the EMT network in three alternative ways: mRNA-Seq and miRNA-Seq, DNA methylation, and CNA and miRNA-Seq. Each mapping was employed to extract five different sets of features using discretization and network-based biomarker identification methods. Each feature set was then used to predict prognosis with SVM and logistic regression classifiers. We measured the prediction accuracy with AUC and AUPR values using 10 times 10-fold cross validation. For a more comprehensive evaluation, we also measured the prediction accuracies of clinical features, EMT plus clinical features, randomly picked 87 molecules from each data mapping, and using all molecules from each data type. Counter-intuitively, EMT features do not always outperform randomly selected features and the prediction accuracies of the five feature sets are mostly not significantly different. Clinical features are shown to give the highest prediction accuracies. In addition, the prediction accuracies of both EMT features and random features are comparable as using all features (more than 17,000) from each data type. Y1 - 2017 U6 - https://doi.org/http://dx.doi.org/10.18547/gcb.2017.vol3.iss3.e57 VL - 3 IS - 3 SP - 1 EP - 13 ER - TY - JOUR A1 - Conrad, Tim A1 - Genzel, Martin A1 - Cvetkovic, Nada A1 - Wulkow, Niklas A1 - Vybiral, Jan A1 - Kutyniok, 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 Bioinformatics N2 - Motivation: 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 how MS spectra dier 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 to noise and outliers, and the identied feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of Compressed Sensing that allows to identify a minimal discriminating set of features from mass spectrometry data-sets. We show how our method performs on artificial and real-world data-sets. Y1 - 2017 U6 - https://doi.org/10.1186/s12859-017-1565-4 VL - 18 IS - 160 SP - 1 EP - 20 ER - TY - JOUR A1 - Seeber, L. A1 - Conrad, Tim A1 - Hoppe, Christian A1 - Obermeier, Patrick A1 - Chen, X. A1 - Karsch, K. A1 - Muehlhans, S. A1 - Tief, Franziska A1 - Boettcher, Sindy A1 - Diedrich, S. A1 - Schweiger, Brunhilde A1 - Rath, Barbara T1 - Educating parents about the vaccination status of their children: A user-centered mobile application JF - Preventive Medicine Reports N2 - Parents are often uncertain about the vaccination status of their children. In times of vaccine hesitancy, vaccination programs could benefit from active patient participation. The Vaccination App (VAccApp) was developed by the Vienna Vaccine Safety Initiative, enabling parents to learn about the vaccination status of their children, including 25 different routine, special indication and travel vaccines listed in the WHO Immunization Certificate of Vaccination (WHO-ICV). Between 2012 and 2014, the VAccApp was validated in a hospital-based quality management program in Berlin, Germany, in collaboration with the Robert Koch Institute. Parents of 178 children were asked to transfer the immunization data of their children from the WHO-ICV into the VAccApp. The respective WHO-ICV was photocopied for independent, professional data entry (gold standard). Demonstrating the status quo in vaccine information reporting, a Recall Group of 278 parents underwent structured interviews for verbal immunization histories, without the respective WHO-ICV. Only 9% of the Recall Group were able to provide a complete vaccination status; on average 39% of the questions were answered correctly. Using the WHO-ICV with the help of the VAccApp resulted in 62% of parents providing a complete vaccination status; on average 95% of the questions were answered correctly. After using the VAccApp, parents were more likely to remember key aspects of the vaccination history. User-friendly mobile applications empower parents to take a closer look at the vaccination record, thereby taking an active role in providing accurate vaccination histories. Parents may become motivated to ask informed questions and to keep vaccinations up-to-date. Y1 - 2017 U6 - https://doi.org/10.1016/j.pmedr.2017.01.002 VL - 5 SP - 241 EP - 250 ER - TY - JOUR A1 - Obermeier, Patrick A1 - Heim, A. A1 - Biere, Barbara A1 - Hage, E. A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Schweiger, Brunhilde A1 - Rath, Barbara T1 - Clinical characteristics and disease severity associated with adenovirus infections in infants and children - discovery of a novel adenovirus, HAdV-D80 JF - Clinical Infectious Diseases Y1 - 2018 ER - TY - JOUR A1 - Kork, F. A1 - Spies, Claudia A1 - Conrad, Tim A1 - Weiss, B. A1 - Roenneberg, T. A1 - Wernecke, K.-D. A1 - Balzer, Felix T1 - Associations of postoperative mortality with the time of day, week and year JF - Anaesthesia Y1 - 2018 ER - TY - JOUR A1 - Mireles, Victor A1 - Conrad, Tim T1 - Reusable building blocks in biological systems JF - Journal of the Royal Society Interface N2 - One of the most widely recognized features of biological systems is their modularity. The modules that constitute biological systems are said to be redeployed and combined across several conditions, thus acting as building blocks. In this work, we analyse to what extent are these building blocks reusable as compared with those found in randomized versions of a system. We develop a notion of decompositions of systems into phenotypic building blocks, which allows them to overlap while maximizing the number of times a building block is reused across several conditions. Different biological systems present building blocks whose reusability ranges from single use (e.g. condition specific) to constitutive, although their average reusability is not always higher than random equivalents of the system. These decompositions reveal a distinct distribution of building block sizes in real biological systems. This distribution stems, in part, from the peculiar usage pattern of the elements of biological systems, and constitutes a new angle to study the evolution of modularity. Y1 - 2018 U6 - https://doi.org/10.1098/rsif.2018.0595 VL - 15 IS - 149 SP - 1 EP - 10 ER -