title: Genome-scale protein function prediction in yeast Saccharomyces cerevisiae through integrating multiple sources of high-throughput data.
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.
authors.affiliation: UT-ORNL Graduate School of Genome Science and Technology, Oak Ridge, TN, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Chen
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D004271:DNA, Fungal / Q000235:genetics; D005656:Fungal Proteins / Q000235:genetics*; D015966:Gene Expression Regulation, Fungal; D016681:Genome, Fungal; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D011336:Probability; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics*
chemical_list: D004271:DNA, Fungal; D005656:Fungal Proteins