Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae.

ABSTRACT: As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems on the genomic scale. Discovering new biological knowledge from the high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein functional annotation in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including yeast two-hybrid data, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data, and coded the relationship into 'functiona

SUBMITTER: Chen Y 

PROVIDER: S-EPMC535686 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature