@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} } @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} } @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} } @article{RamsConrad2020, author = {Rams, Mona and Conrad, Tim}, title = {Dictionary Learning for transcriptomics data reveals type-specific gene modules in a multi-class setting}, volume = {62}, journal = {it - Information Technology}, number = {3-4}, publisher = {De Gruyter}, address = {Oldenbourg}, issn = {2196-7032}, doi = {https://doi.org/10.1515/itit-2019-0048}, year = {2020}, language = {en} } @article{RathConradMylesetal.2017, author = {Rath, Barbara and Conrad, Tim and Myles, Puja and Alchikh, Maren and Ma, Xiaolin and Hoppe, Christian and Tief, Franziska and Chen, Xi and Obermeier, Patrick and Kisler, Bron and Schweiger, Brunhilde}, title = {Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials}, volume = {15}, journal = {Expert Review of Anti-infective Therapy}, number = {6}, doi = {10.1080/14787210.2017.1295847}, pages = {545 -- 568}, year = {2017}, abstract = {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.}, language = {en} } @article{ShaoCannistraciConrad2017, author = {Shao, Borong and Cannistraci, Carlo Vittorio 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 = {10.18547/gcb.2017.vol3.iss3.e57}, pages = {e57}, 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} }