Global Propagation Method for Predicting Protein Function by Integrating Multiple Data Sources

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 naïve 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.