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ABSTRACT: Determination of protein function and biological pathway is one of the most challenging problems in the post-genomic era. To address this challenge, we have developed a new integrated probabilistic method for cellular function prediction using microarray gene expression profiles, in conjunction with predicted protein-protein interactions and annotations of known proteins. Our approach is based on a novel assessment for the relationship between correlation of two genes' expression profiles and their functional relationship in terms of the Gene Ontology (GO) hierarchy. We applied the method for function prediction of hypothetical genes in Arabidopsis. We have also extended our method using Dijkstra's algorithm to identify the components and topology of signaling pathway of phosphatidic acid as a second messenger in Arabidopsis.
International Journal of Bioinformatics Research and Applications 02/2005; 1(3):335-50.
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IJBRA. 01/2005; 1:335-350.
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ABSTRACT: We have developed a new integrated probabilistic method for cellular function prediction by using microarray gene expression profiles, in conjunction with predicted protein-protein interactions and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between correlation of two genes' expression profiles and their functional relationship in terms of the gene ontology (GO) hierarchy. We applied the method for function predictions of hypothetical genes in Arabidopsis. We have also extended our computational method using Dijkstra's algorithm to identify the components and topology of a pathway, and we applied it for predicting the signaling pathway of phosphatidic acid as a second messenger in Arabidopsis.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2004; 4:2881-4.
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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.
Omics A Journal of Integrative Biology 02/2004; 8(4):322-33. · 2.44 Impact Factor
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