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A new computational approach to analyze human protein complexes and predict novel protein interactions.


ABSTRACT: We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated alpha-tubulin and early endosome antigen 1 as its novel interactors.

SUBMITTER: Zanivan S 

PROVIDER: S-EPMC2246258 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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A new computational approach to analyze human protein complexes and predict novel protein interactions.

Zanivan Sara S   Cascone Ilaria I   Peyron Chiara C   Molineris Ivan I   Marchio Serena S   Caselle Michele M   Bussolino Federico F  

Genome biology 20070101 12


We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-  ...[more]

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