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- W2092165279 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." @default.
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- W2092165279 date "2004-12-01" @default.
- W2092165279 modified "2023-09-23" @default.
- W2092165279 title "Genome-Scale Gene Function Prediction Using Multiple Sources of High-Throughput Data in Yeast <i>Saccharomyces cerevisiae</i>" @default.
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- W2092165279 doi "https://doi.org/10.1089/omi.2004.8.322" @default.
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