Detangling PPI Networks to Uncover Functionally Meaningful Clusters
Pages 752 - 753
Abstract
We compare computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO). We compare the performance of three popular community detection algorithms with the same algorithms run after the network is pre-processed by removing and reweighting based on the diffusion state distance (DSD) between pairs of nodes in the network. We call this ``detangling'' the network. In almost all cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters.
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Published In
August 2017
800 pages
ISBN:9781450347228
DOI:10.1145/3107411
- General Chairs:
- Nurit Haspel,
- Lenore J. Cowen,
- Program Chairs:
- Amarda Shehu,
- Tamer Kahveci,
- Giuseppe Pozzi
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 20 August 2017
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BCB '17
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BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
August 20 - 23, 2017
Massachusetts, Boston, USA
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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;
Overall Acceptance Rate 254 of 885 submissions, 29%
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