Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile |
| |
Authors: | Gao Lei Li Xia Guo Zheng Zhu MingZhu Li YanHui Rao ShaoQi |
| |
Affiliation: | 1.Department of Bioinformatics,Harbin Medical University,Harbin,China;2.School of Biology Science and Technology,Tongji University,Shanghai,China;3.Departments of Molecular Cardiology and Cardiovascular Medicine,the Cleveland Clinic Foundation,Cleveland,USA |
| |
Abstract: | 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 interaction 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
automatically selects the most appropriate functional classes as specific as possible during the learning process, 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 organized 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. |
| |
Keywords: | gene expression profile protein-protein interaction Gene Ontology similarity gene function prediction |
本文献已被 万方数据 SpringerLink 等数据库收录! |
|