Journal
BIOINFORMATICS
Volume 38, Issue 12, Pages 3245-3251Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac312
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Funding
- Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) [G046318, G.0371.06, 3G045620]
- VLAIO (Flanders Innovation & Entrepreneurship) [HBC.2019.2528]
- UGent Bijzonder Onderzoeksfonds
- Flemish Government (AI Research Program)
- KU Leuven Bijzonder Onderzoeksfonds
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This study introduces a logic programming framework called OMEN, which effectively combines gene-specific driver properties and gene-set properties to improve the specificity of driver gene detection. It robustly identifies driver genes and modules as proxies of driver pathways.
Motivation: Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed. Results: We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark dataset derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.
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