Reference: Chen Y and Xu D (2005) Genome-scale protein function prediction in yeast Saccharomyces cerevisiae through integrating multiple sources of high-throughput data. Pac Symp Biocomput 471-82

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Abstract


As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. Discovering new biological knowledge from high-throughput biological data is a major challenge for bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein function prediction in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including protein binary interactions, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data. Based on our method, 1802 out of 2280 unannotated proteins in the yeast were assigned functions systematically. The related computer package is available upon request.

Reference Type
Journal Article | Research Support, U.S. Gov't, Non-P.H.S.
Authors
Chen Y, Xu D
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