TY - JOUR A1 - Shao, Borong A1 - Bjaanaes, Maria A1 - Helland, Aslaug A1 - Schütte, Christof A1 - Conrad, Tim T1 - EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma JF - PLOS ONE N2 - 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. Y1 - 2019 U6 - https://doi.org/10.1371/journal.pone.0204186 VL - 14 IS - 1 ER - TY - JOUR A1 - Alchikh, Maren A1 - Conrad, Tim A1 - Ma, Xiaolin A1 - Broberg, Eeva K. A1 - Penttinen, P. A1 - Reiche, J. A1 - Biere, Barbara A1 - Schweiger, Brunhilde A1 - Rath, Barbara A1 - Hoppe, Christian T1 - Are we missing respiratory viral infections in infants and children? Comparison of a hospital-based quality management system with standard of care JF - Clinical Microbiology and Infection Y1 - 2019 U6 - https://doi.org/10.1016/j.cmi.2018.05.023 SN - 1469-0691 VL - 25 IS - 3 SP - 380.e9 EP - 380.e16 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 -