Format

Send to:

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2008 Nov 15;24(22):2608-14. doi: 10.1093/bioinformatics/btn498. Epub 2008 Oct 1.

Physical protein-protein interactions predicted from microarrays.

Author information

  • 1Columbia University Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. ts2186@columbia.edu

Abstract

MOTIVATION:

Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task.

RESULTS:

We validated our support vector machine-based method on several independent datasets. At the same levels of accuracy, our method recovered more experimentally observed physical interactions than a conventional correlation-based approach. Pairs predicted by our method to very likely interact were close in the overall network of interaction, suggesting our method as an aid for functional annotation. We applied the method to predict interactions in yeast (Saccharomyces cerevisiae). A Gene Ontology function annotation analysis and literature search revealed several probable and novel predictions worthy of future experimental validation. We therefore hope our new method will improve the annotation of interactions as one component of multi-source integrated systems.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
18829707
[PubMed - indexed for MEDLINE]
PMCID:
PMC2579715
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk