An integrative network approach to map the transcriptome to the phenome

Michael R. Mehan, Juan Nunez-Iglesias, Mrinal Kalakrishnan, Michael S. Waterman, Xianghong Jasmine Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


Although many studies have been successful in the discovery of cooperating groups of genes, mapping these groups to phenotypes has proved a much more challenging task. In this paper, we present the first genome-wide mapping of gene coexpression modules onto the phenome. We annotated coexpression networks from 136 microarray datasets with phenotypes from the Unified Medical Language System (UMLS). We then designed an efficient graph-based simulated annealing approach to identify coexpression modules frequently and specifically occurring in datasets related to individual phenotypes. By requiring phenotype-specific recurrence, we ensure the robustness of our findings. We discovered 9,183 modules specific to 47 phenotypes, and developed validation tests combining Gene Ontology, GeneRIF and UMLS. Our method is generally applicable to any kind of abundant network data with defined phenotype association, and thus paves the way for genome-wide, gene network-phenotype maps.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 12th Annual International Conference, RECOMB 2008, Proceedings
Number of pages14
Publication statusPublished - 21 Jul 2008
Externally publishedYes
EventInternational Conference on Computational Molecular Biology 2008 - Singapore, Singapore
Duration: 30 Mar 20082 Apr 2008
Conference number: 12th (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4955 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Computational Molecular Biology 2008
Abbreviated titleRECOMB 2008
Internet address

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