@article{SarichDjurdjevacConradBruckneretal.2014, author = {Sarich, Marco and Djurdjevac Conrad, Natasa and Bruckner, Sharon and Conrad, Tim and Sch{\"u}tte, Christof}, title = {Modularity revisited: A novel dynamics-based concept for decomposing complex networks}, volume = {1}, journal = {Journal of Computational Dynamics}, number = {1}, doi = {10.3934/jcd.2014.1.191}, pages = {191 -- 212}, year = {2014}, language = {en} } @article{DjurdjevacConradBrucknerConradetal.2012, author = {Djurdjevac Conrad, Natasa and Bruckner, Stefanie and Conrad, Tim and Sch{\"u}tte, Christof}, title = {Random Walks on Complex Modular Networks}, volume = {6}, journal = {Journal of Numerical Analysis, Industrial and Applied Mathematics}, number = {1-2}, publisher = {European Society of Computational Methods in Sciences and Engineering}, pages = {29 -- 50}, year = {2012}, language = {en} } @article{WulkowConradDjurdjevacConradetal.2021, author = {Wulkow, Hanna and Conrad, Tim and Djurdjevac Conrad, Natasa and M{\"u}ller, Sebastian A. and Nagel, Kai and Sch{\"u}tte, Christof}, title = {Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts}, volume = {16}, journal = {PLOS One}, number = {4}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0249676}, year = {2021}, language = {en} } @article{BlaskovicConradKlusetal.2025, author = {Blaskovic, Filip and Conrad, Tim and Klus, Stefan and Djurdjevac Conrad, Natasa}, title = {Random walk based snapshot clustering for detecting community dynamics in temporal networks}, volume = {15}, journal = {Scientific Reports}, arxiv = {http://arxiv.org/abs/2412.12187}, doi = {10.1038/s41598-025-09340-0}, pages = {24414}, year = {2025}, abstract = {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.}, language = {en} } @article{BorongCannistraciConrad2017, author = {Borong, Shao and Cannistraci, Carlo and Conrad, Tim}, title = {Epithelial Mesenchymal Transition Network-based Feature Engineering in Lung Adenocarcinoma Prognosis Prediction Using Multiple Omic Data}, volume = {3}, journal = {Genomics and Computational Biology}, number = {3}, doi = {http://dx.doi.org/10.18547/gcb.2017.vol3.iss3.e57}, pages = {1 -- 13}, year = {2017}, abstract = {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.}, language = {en} } @article{ConradGenzelCvetkovicetal.2017, author = {Conrad, Tim and Genzel, Martin and Cvetkovic, Nada and Wulkow, Niklas and Vybiral, Jan and Kutyniok, Gitta and Sch{\"u}tte, Christof}, title = {Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data}, volume = {18}, journal = {BMC Bioinformatics}, number = {160}, doi = {10.1186/s12859-017-1565-4}, pages = {1 -- 20}, year = {2017}, abstract = {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.}, language = {en} } @article{SeeberConradHoppeetal.2017, author = {Seeber, L. and Conrad, Tim and Hoppe, Christian and Obermeier, Patrick and Chen, X. and Karsch, K. and Muehlhans, S. and Tief, Franziska and Boettcher, Sindy and Diedrich, S. and Schweiger, Brunhilde and Rath, Barbara}, title = {Educating parents about the vaccination status of their children: A user-centered mobile application}, volume = {5}, journal = {Preventive Medicine Reports}, doi = {10.1016/j.pmedr.2017.01.002}, pages = {241 -- 250}, year = {2017}, abstract = {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.}, language = {en} } @article{ObermeierHeimBiereetal.2018, author = {Obermeier, Patrick and Heim, A. and Biere, Barbara and Hage, E. and Alchikh, Maren and Conrad, Tim and Schweiger, Brunhilde and Rath, Barbara}, title = {Clinical characteristics and disease severity associated with adenovirus infections in infants and children - discovery of a novel adenovirus, HAdV-D80}, journal = {Clinical Infectious Diseases}, year = {2018}, language = {en} } @article{KorkSpiesConradetal.2018, author = {Kork, F. and Spies, Claudia and Conrad, Tim and Weiss, B. and Roenneberg, T. and Wernecke, K.-D. and Balzer, Felix}, title = {Associations of postoperative mortality with the time of day, week and year}, journal = {Anaesthesia}, year = {2018}, language = {en} } @article{MirelesConrad2018, author = {Mireles, Victor and Conrad, Tim}, title = {Reusable building blocks in biological systems}, volume = {15}, journal = {Journal of the Royal Society Interface}, number = {149}, doi = {10.1098/rsif.2018.0595}, pages = {1 -- 10}, year = {2018}, abstract = {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.}, language = {en} } @article{MaConradAlchikhetal.2018, author = {Ma, Xiaolin and Conrad, Tim and Alchikh, Maren and Reiche, J. and Schweiger, Brunhilde and Rath, Barbara}, title = {Can we distinguish respiratory viral infections based on clinical features? A prospective pediatric cohort compared to systematic literature review}, volume = {28}, journal = {Medical Virology}, number = {5}, doi = {10.1002/rmv.1997}, pages = {1 -- 12}, year = {2018}, abstract = {Studies have shown that the predictive value of "clinical diagnoses" of influenza and other respiratory viral infections is low, especially in children. In routine care, pediatricians often resort to clinical diagnoses, even in the absence of robust evidence-based criteria. We used a dual approach to identify clinical characteristics that may help to differentiate infections with common pathogens including influenza, respiratory syncytial virus, adenovirus, metapneumovirus, rhinovirus, bocavirus-1, coronaviruses, or parainfluenza virus: (a) systematic review and meta-analysis of 47 clinical studies published in Medline (June 1996 to March 2017, PROSPERO registration number: CRD42017059557) comprising 49 858 individuals and (b) data-driven analysis of an inception cohort of 6073 children with ILI (aged 0-18 years, 56\% male, December 2009 to March 2015) examined at the point of care in addition to blinded PCR testing. We determined pooled odds ratios for the literature analysis and compared these to odds ratios based on the clinical cohort dataset. This combined analysis suggested significant associations between influenza and fever or headache, as well as between respiratory syncytial virus infection and cough, dyspnea, and wheezing. Similarly, literature and cohort data agreed on significant associations between HMPV infection and cough, as well as adenovirus infection and fever. Importantly, none of the abovementioned features were unique to any particular pathogen but were also observed in association with other respiratory viruses. In summary, our "real-world" dataset confirmed published literature trends, but no individual feature allows any particular type of viral infection to be ruled in or ruled out. For the time being, laboratory confirmation remains essential. More research is needed to develop scientifically validated decision models to inform best practice guidelines and targeted diagnostic algorithms.}, language = {en} } @article{AlchikhConradHoppeetal.2018, author = {Alchikh, Maren and Conrad, Tim and Hoppe, Christian and Ma, Xiaolin and Broberg, Eeva K. and Penttinen, P. and Reiche, J. and Biere, Barbara and Schweiger, Brunhilde and Rath, Barbara}, title = {Are we missing respiratory viral infections in infants and children? Comparison of a hospital-based quality management system with standard of care. Clinical Microbiology and Infection}, journal = {Clinical Microbiology and Infection}, number = {06/18}, doi = {10.1016/j.cmi.2018.05.023}, pages = {1 -- 1}, year = {2018}, language = {en} } @inproceedings{JayrannejadConrad2017, author = {Jayrannejad, Fahrnaz and Conrad, Tim}, title = {Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining}, booktitle = {Springer Lecture Notes in Artificial Intelligence}, pages = {studi}, year = {2017}, abstract = {Recent advances in -omics technology has yielded in large data-sets in many areas of biology, such as mass spectrometry based proteomics. However, analyzing this data is still a challenging task mainly due to the very high dimensionality and high noise content of the data. One of the main objectives of the analysis is the identification of relevant patterns (or features) which can be used for classification of new samples to healthy or diseased. So, a method is required to find easily interpretable models from this data. To gain the above mentioned goal, we have adapted the disjunctive association rule mining algorithm, TitanicOR, to identify emerging patterns from our mass spectrometry proteomics data-sets. Comparison to five state-of-the-art methods shows that our method is advantageous them in terms of identifying the inter-dependency between the features and the TP-rate and precision of the features selected. We further demonstrate the applicability of our algorithm to one previously published clinical data-set.}, language = {en} } @article{ShaoBjaanaesHellandetal.2019, author = {Shao, Borong and Bjaanaes, Maria and Helland, Aslaug and Sch{\"u}tte, Christof and Conrad, Tim}, title = {EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma}, volume = {14}, journal = {PLOS ONE}, number = {1}, doi = {10.1371/journal.pone.0204186}, year = {2019}, abstract = {Various feature selection algorithms have been proposed to identify cancer prognostic biomarkers. In recent years, however, their reproducibility is criticized. The performance of feature selection algorithms is shown to be affected by the datasets, underlying networks and evaluation metrics. One of the causes is the curse of dimensionality, which makes it hard to select the features that generalize well on independent data. Even the integration of biological networks does not mitigate this issue because the networks are large and many of their components are not relevant for the phenotype of interest. With the availability of multi-omics data, integrative approaches are being developed to build more robust predictive models. In this scenario, the higher data dimensions create greater challenges. We proposed a phenotype relevant network-based feature selection (PRNFS) framework and demonstrated its advantages in lung cancer prognosis prediction. We constructed cancer prognosis relevant networks based on epithelial mesenchymal transition (EMT) and integrated them with different types of omics data for feature selection. With less than 2.5\% of the total dimensionality, we obtained EMT prognostic signatures that achieved remarkable prediction performance (average AUC values above 0.8), very significant sample stratifications, and meaningful biological interpretations. In addition to finding EMT signatures from different omics data levels, we combined these single-omics signatures into multi-omics signatures, which improved sample stratifications significantly. Both single- and multi-omics EMT signatures were tested on independent multi-omics lung cancer datasets and significant sample stratifications were obtained.}, language = {en} } @article{AicheReinertSchuetteetal.2012, author = {Aiche, Stephan and Reinert, Knut and Sch{\"u}tte, Christof and Hildebrand, Diana and Schl{\"u}ter, Hartmut and Conrad, Tim}, title = {Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs}, volume = {7}, journal = {PLoS ONE}, number = {7}, publisher = {Public Library of Science}, doi = {10.1371/journal.pone.0040656}, pages = {e40656}, year = {2012}, language = {en} } @article{FiedlerLeichtleKaseetal.2009, author = {Fiedler, Georg Martin and Leichtle, Alexander Benedikt and Kase, Julia and Baumann, Sven and Ceglarek, Uta and Felix, Klaus and Conrad, Tim and Witzigmann, Helmut and Weimann, Arved and Sch{\"u}tte, Christof and Hauss, Johann and B{\"u}chler, Markus and Thiery, Joachim}, title = {Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated With Pancreatic Cancer}, volume = {15}, journal = {Clinical Cancer Research}, number = {11}, publisher = {American Association for Cancer Research,}, doi = {10.1158/1078-0432.CCR-08-2701}, pages = {3812 -- 3819}, year = {2009}, language = {en} } @article{ConradLeichtleHagehuelsmannetal.2006, author = {Conrad, Tim and Leichtle, Alexander Benedikt and Hageh{\"u}lsmann, Andre and Diederichs, Elmar and Baumann, Sven and Thiery, Joachim and Sch{\"u}tte, Christof}, title = {Beating the Noise}, volume = {4216}, journal = {Lecture Notes in Computer Science}, publisher = {Springer}, pages = {119 -- 128}, year = {2006}, language = {en} } @misc{AustConradRempelDegkwitzetal.2015, author = {Aust, Sonja and Conrad-Rempel, Steffi and Degkwitz, Andreas and Golz, Bettina and Hasler, Tim and Heiss, Alexandra and Keidel, Petra and Koch, Thorsten and Kuberek, Monika and Lill, Monika and Lohrum, Stefan and M{\"u}ller, Anja and Peters-Kottig, Wolfgang and Quitzsch, Nicole and Rusch, Beate and Schwidder, Jens and Sischke, Gabriele and Stanek, Ursula and Taylor, Viola and Weihe, Signe and Wyrwol, Christiane and Happel, Hans-Gerd}, title = {KOBV Jahresbericht 2013-2014}, volume = {2013-2014}, number = {2013-2014}, organization = {Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV)}, issn = {0934-5892}, pages = {1 -- 56}, year = {2015}, abstract = {Der KOBV-Jahresbericht informiert r{\"u}ckblickend im 2-Jahres-Rhythmus {\"u}ber die bibliothekarisch-fachlichen Entwicklungen im Verbund und die Projekte des Kooperativen Bibliotheksverbunds Berlin-Brandenburg (KOBV).}, language = {de} } @misc{ChristofConradRempelDegkwitzetal.2017, author = {Christof, J{\"u}rgen and Conrad-Rempel, Steffi and Degkwitz, Andreas and Goltz-Fellgiebel, Julia Alexandra and Happel, Hans-Gerd and Hasler, Tim and Heiss, Alexandra and Kaminsky, Uta and Kant, Oliver and Kende, Jiř{\´i} and Koch, Thorsten and Kuberek, Monika and Lohrum, Stefan and M{\"u}ller, Anja and Mutter, Moritz and Peters-Kottig, Wolfgang and Quitzsch, Nicole and Rusch, Beate and Schwidder, Jens and Stanek, Ursula and Weihe, Signe and Zeyns, Andrea}, title = {KOBV Jahresbericht 2015-2016}, volume = {2015-2016}, number = {2015-2016}, organization = {Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV)}, issn = {0934-5892}, pages = {1 -- 64}, year = {2017}, abstract = {Der KOBV-Jahresbericht informiert r{\"u}ckblickend im 2-Jahres-Rhythmus {\"u}ber die bibliothekarisch-fachlichen Entwicklungen im Verbund und die Projekte des Kooperativen Bibliotheksverbunds Berlin-Brandenburg (KOBV).}, language = {de} } @article{AlchikhConradMaetal.2019, author = {Alchikh, Maren and Conrad, Tim and Ma, Xiaolin and Broberg, Eeva K. and Penttinen, P. and Reiche, J. and Biere, Barbara and Schweiger, Brunhilde and Rath, Barbara and Hoppe, Christian}, title = {Are we missing respiratory viral infections in infants and children? Comparison of a hospital-based quality management system with standard of care}, volume = {25}, journal = {Clinical Microbiology and Infection}, number = {3}, issn = {1469-0691}, doi = {10.1016/j.cmi.2018.05.023}, pages = {380.e9 -- 380.e16}, year = {2019}, language = {en} }