Combining gene expression profiles and protein-protein interaction data to infer gene functions

Journal of Biotechnology
Kang TuYi-Xue Li

Abstract

The ever-increasing flow of gene expression profiles and protein-protein interactions has catalyzed many computational approaches for inference of gene functions. Despite all the efforts, there is still room for improvement, for the information enriched in each biological data source has not been exploited to its fullness. A composite method is proposed for classifying unannotated genes based on expression data and protein-protein interaction (PPI) data, which extracts information from both data sources in novel ways. With the noise nature of expression data taken into consideration, importance is attached to the consensus expression patterns of gene classes instead of the actual expression profiles of individual genes, thus characterizing the composite method with enhanced robustness against microarray data variation. With regard to the PPI network, the traditional clear-cut binary attitude towards inter- and intra-functional interactions is abandoned, whereas a more objective perspective into the PPI network structure is formed through incorporating the varied function-function interaction probabilities into the algorithm. The composite method was implemented in two numerical experiments, where its improvement over single-dat...Continue Reading

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Citations

Oct 25, 2007·BMC Bioinformatics·Ioannis A MaraziotisAnastasios Bezerianos
Nov 4, 2008·BMC Systems Biology·Ioannis A MaraziotisAnastasios Bezerianos
Dec 17, 2008·Future Microbiology·Rosalía Diez-Orejas, Elena Fernández-Arenas
Mar 19, 2014·BMC Research Notes·Oussema SouiaiAlia Benkahla
Oct 10, 2009·Plant Signaling & Behavior·Sorina C PopescuSavithramma P Dinesh-Kumar

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