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Nucleic Acids Research 2004 32(21):6414-6424; doi:10.1093/nar/gkh978
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Published online 7 December 2004

Nucleic Acids Research, Vol. 32 No. 21 © Oxford University Press 2004; all rights reserved

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

Yu Chen1,2 and Dong Xu1,2,*

1 UT-ORNL Graduate School of Genome Science and Technology, Oak Ridge, TN, USA and 2 Digital Biology Laboratory, Computer Science Department, 201 Engineering Building West, University of Missouri, Columbia, MO 65211, USA

* To whom correspondence should be addressed. Tel: +1 573 882 7064; Fax: +1 573 882 8318; Email: xudong{at}missouri.edu
Present address: Yu Chen, BioMarker Development, Novartis Pharmaceuticals Corp., One Health Plaza, East Hanover, NJ 07936, USA

Received October 11, 2004; Revised and Accepted November 15, 2004

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 ‘functional linkage graph’, where each node represents one protein and the weight of each edge is characterized by the Bayesian probability of function similarity between two proteins. We also integrated the evolution information and protein subcellular localization information into the prediction. Based on our method, 1802 out of 2280 unannotated proteins in yeast were assigned functions systematically.


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