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  • Title: Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae.
    Author: Joshi T, Chen Y, Becker JM, Alexandrov N, Xu D.
    Journal: OMICS; 2004; 8(4):322-33. PubMed ID: 15703479.
    Abstract:
    Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.
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