Robust disease module mining via enumeration of diverse prize-collecting Steiner trees

Bernett J, Krupke D, Sadegh S, Baumbach J, Fekete SP, Kacprowski T, List M, Blumenthal DB (2022)

Publication Language: English

Publication Status: Published

Publication Type: Journal article

Publication year: 2022

Journal

Bioinformatics Oxford University Press (OUP): Policy B - Oxford Open Option B

Book Volume: 38

Pages Range: 1600-1606

Journal Issue: 6

URI: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab876/6497106?guestAccessKey=27c2a19b-11b5-4c3c-88d5-f1131e3809a9

DOI: 10.1093/bioinformatics/btab876

Abstract

Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically include non-robust steps in their workflows, i. e., the computed subnetworks vary when running the DMMMs multiple times on equivalent input. This lack of robustness has a negative effect on the trustworthiness of the obtained subnetworks and is hence detrimental for the wide-spread adoption of DMMMs in the biomedical sciences.\nTo overcome this problem, we present a new DMMM called ROBUST (robust disease module mining via enumeration of diverse prize-collecting Steiner trees). In a large-scale empirical evaluation, we show that ROBUST outperforms competing methods in terms of robustness, scalability and, in most settings, functional relevance of the produced modules, measured via KEGG gene set enrichment scores and overlap with DisGeNET disease genes.\nA Python 3 implementation and scripts to reproduce the results reported in this paper are available on GitHub: https://github.com/bionetslab/robust, https://github.com/bionetslab/robust-eval.\nSupplementary data are available at Bioinformatics online.\nMOTIVATION\nRESULTS\nAVAILABILITY\nSUPPLEMENTARY INFORMATION

Authors with CRIS profile

David Blumenthal Professur für Biomedical Network Science

Involved external institutions

Universität Hamburg (UHH) DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Technische Universität Braunschweig DE Germany (DE)

How to cite

APA:

Bernett, J., Krupke, D., Sadegh, S., Baumbach, J., Fekete, S.P., Kacprowski, T.,... Blumenthal, D.B. (2022). Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics, 38(6), 1600-1606. https://dx.doi.org/10.1093/bioinformatics/btab876

MLA:

Bernett, Judith, et al. "Robust disease module mining via enumeration of diverse prize-collecting Steiner trees." Bioinformatics 38.6 (2022): 1600-1606.

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