Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks

BMC Bioinformatics

Research

Jihong Guan¹  Hui Liu²  Shuigeng Zhou³  Wei Xiong³ 

[1] Department of Computer Science & Technology, Tongji University, Shanghai, China;Research Lab of Information Management, Changzhou University, Jiangsu, China;School of Computer Science, and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China;

关键词: Gene Ontology;    Prediction Performance;    Basic Local Alignment Search Tool;    Annotate Protein;    Protein Versus;   

DOI  :  10.1186/1471-2105-14-S12-S4

来源: Springer

【 摘 要 】

BackgroundProtein function prediction is an important problem in the post-genomic era. Recent advances in experimental biology have enabled the production of vast amounts of protein-protein interaction (PPI) data. Thus, using PPI data to functionally annotate proteins has been extensively studied. However, most existing network-based approaches do not work well when annotation and interaction information is inadequate in the networks.ResultsIn this paper, we proposed a new method that combines PPI information and protein sequence information to boost the prediction performance based on collective classification. Our method divides function prediction into two phases: First, the original PPI network is enriched by adding a number of edges that are inferred from protein sequence information. We call the added edges implicit edges, and the existing ones explicit edges correspondingly. Second, a collective classification algorithm is employed on the new network to predict protein function.ConclusionsWe conducted extensive experiments on two real, publicly available PPI datasets. Compared to four existing protein function prediction approaches, our method performs better in many situations, which shows that adding implicit edges can indeed improve the prediction performance. Furthermore, the experimental results also indicate that our method is significantly better than the compared approaches in sparsely-labeled networks, and it is robust to the change of the proportion of annotated proteins.

【 授权许可】

Unknown   
© Xiong et al; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.