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Bioinformatics Advance Access originally published online on June 28, 2007
Bioinformatics 2007 23(17):2322-2330; doi:10.1093/bioinformatics/btm332
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Context-sensitive data integration and prediction of biological networks

Chad L. Myers 1,2 and Olga G. Troyanskaya 1,2,*

1Department of Computer Science, Princeton University, 35 Olden Street and 2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA

*To whom correspondence should be addressed.


  Abstract

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.

Availability: A software implementation of our approach is available on request from the authors.

Contact: ogt{at}genomics.princeton.edu

Supplementary information: Supplementary data are available at http://avis.princeton.edu/contextPIXIE/

Associate Editor: Martin Bishop

Received on April 10, 2007; revised on June 1, 2007; accepted on June 18, 2007

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