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MotivationUnsupervised learning approaches are frequently used to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups.ResultsWe developed the network-constrained biclustering approach Biclustering Constrained by Networks (BiCoN) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface.Availability and implementationPyPI package: https://pypi.org/project/bicon.Web interfacehttps://exbio.wzw.tum.de/bicon.Supplementary informationSupplementary data are available at Bioinformatics online.

Original publication

DOI

10.1093/bioinformatics/btaa1076

Type

Journal

Bioinformatics (Oxford, England)

Publication Date

08/2021

Volume

37

Pages

2398 - 2404

Addresses

Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Weihenstephan, 80333 Munich, Germany.