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论文类型： 期刊论文

发表时间： 2016-01-01

发表刊物： CURRENT BIOINFORMATICS

收录刊物： SCIE、Scopus

卷号： 11

期号： 2

页面范围： 186-194

ISSN号： 1574-8936

关键字： Data integration; gene ontology; global propagation algorithm; label correlation; protein function prediction; yeast

摘要： Protein function prediction is one of the most important tasks in bioinformatics. Nowadays, high-throughput experiments have generated large scale genomics and proteomics data. To accurately annotate proteins, it is necessary and wise to integrate these heterogeneous data sources. In this paper, a multi-source protein global propagation (MS-PGP) algorithm has been proposed, which integrates multiple data sources and combines protein global propagation with label correlation (PGP) algorithm to predict functions for unannotated proteins. Specifically, we use three data sources to predict protein functions: sequence data, microarray gene expression data and protein-protein interaction data. A naive Bayesian fashion method is adopted to fuse the three data sources into a combined network. Gene ontology biological process annotation is used to calculate the association scores between unannotated proteins and functions. The experimental results on Yeast show that the proposed method has a higher accuracy over other multiple network methods. It is efficient to predict the function of unannotated proteins.