Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile  

作　　者：GAO Lei1, LI Xia1,2, GUO Zheng1,2, ZHU MingZhu1, LI YanHui1 & RAO ShaoQi1,3 1 Department of Bioinformatics, Harbin Medical University, Harbin 150086, China 2 School of Biology Science and Technology, Tongji University, Shanghai 200092, China 3 Departments of Molecular Cardiology and Cardiovascular Medicine, the Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleve-land, Ohio 44195, USA 

出　　处：《Science China(Life Sciences)》2007年第1期125-134,共10页中国科学（生命科学英文版）

基　　金：the National Natural Science Foundation of China (Grant Nos. 30170515, 30370388, 30370798, 30570424 and 30571034),;the National High Tech Development Project of China (Grant Nos. 2003AA2Z2051 and 2002AA2Z2052),;Heilongjiang Science & Technology Key Project (Grant No. GB03C602-4),;Harbin (City) Science & Technology Key Project (Grant No. 2003AA3CS113),;Natural Science Foundation of Heilongjiang (Grant No. F0177 ),;Outstanding Overseas Scientist Foundation of Education Department of Heilongjiang Province (Grant No. 1055HG009)

摘　　要：GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

关 键 词：GENE expression profile PROTEIN-PROTEIN interaction GENE ONTOLOGY SIMILARITY GENE function prediction 

分 类 号：Q51-33[生物学—生物化学] Q75