Characterizing gene functions at the genome scale is one of the most important and challenging tasks in the post-genomic era. To address this challenge, we have developed an integrated probabilistic approach, which combines high-throughput data of protein-protein interactions, protein complexes, microarray gene-expression profiles, and genomic sequences. We quantified the relationship between functional similarity in the Gene Ontology (GO) functions and high-throughput data, and coded the relationship into a "functional linkage graph", where each node represents one gene and the weight of each edge is characterized by the Bayesian probability of function similarity between the two connected genes. Then we applied Boltzmann machine and simulated annealing to perform optimization for assigning gene functions based on the global information of the functional linkage graph. We also integrated the evolution and protein subcellular localization information into the prediction. Furthermore, we employed a meta-analysis approach to combine multiple sets of microarray data, either within the same species or for orthologs across different species for gene function prediction. We have implemented our method into the package GeneFAS and used GeneFAS for systematically assigning gene functions in yeast, Arabidopsis, human, and mouse.
Dong Xu is Chair of Computer Science Department, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign in 1995 and did two-year postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining University of Missouri. Over the past fifteen years, he has published more than 120 scientific papers. Professor Dong Xu is a recipient of the year 2001 R&D 100 award, a prestigious international award sponsored by Research & Development magazine that honors the 100 most significant new technical products of the previous year, for developing "Protein Structure Prediction and Evaluation Computer Toolkit (PROSPECT)". He also received 2003 Award of Excellence in Technology Transfer from The Federal Laboratory Consortium for developing gene expression analysis package EXCAVATOR. He has received research grants from the Department of Energy, the National Science Foundation, the U.S. Department of Agriculture, National Institute of Health, Ceres Inc., and Monsanto Research Funds. He is a member of the Editorial Board for “Current Protein and Peptide Science” and “Applied and Environmental Microbiology”.