Context-sensitive data integration and prediction of biological networks

Chad L. Myers, Olga G. Troyanskaya

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


Motivation: Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context. Results: We confirm the presence of context-dependent variation in functional genomic data and propose a Bayesian approach for context-sensitive integration and query-based recovery of biological process-specific networks. By applying this method to Saccharomyces cerevisiae, we demonstrate that leveraging contextual information can significantly improve the precision of network predictions, including assignment for uncharacterized genes. We expect that this general context-sensitive approach can be applied to other organisms and prediction scenarios.

Original languageEnglish (US)
Pages (from-to)2322-2330
Number of pages9
Issue number17
StatePublished - Sep 1 2007
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to thank Matt Hibbs, Curtis Huttenhower, Florian Markowetz, and Edo Airoldi for insightful discussions. This research is partially supported by NSF CAREER award DBI-0546275 to OGT, NIH grant R01 GM071966, NIH grant T32 HG003284, and NIGMS Center of Excellence grant P50 GM071508. OGT is an Alfred P. Sloan Research Fellow.


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