Collective prediction of protein functions from protein-protein interaction networks

BMC Bioinformatics

Proceedings

Ruichao Shi¹  Qingyao Wu¹  Yunming Ye¹  Michael K Ng²  Shen-Shyang Ho³ 

[1] Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen, China;Department of Mathematics, Hong Kong Baptist University, Hong Kong, China;School of Computer Engineering, Nanyang Technological University, Singapore;

关键词: protein function prediction;    protein-protein interaction network;    collective classification;   

DOI  :  10.1186/1471-2105-15-S2-S9

来源: Springer

【 摘 要 】

BackgroundAutomated assignment of functions to unknown proteins is one of the most important task in computational biology. The development of experimental methods for genome scale analysis of molecular interaction networks offers new ways to infer protein function from protein-protein interaction (PPI) network data. Existing techniques for collective classification (CC) usually increase accuracy for network data, wherein instances are interlinked with each other, using a large amount of labeled data for training. However, the labeled data are time-consuming and expensive to obtain. On the other hand, one can easily obtain large amount of unlabeled data. Thus, more sophisticated methods are needed to exploit the unlabeled data to increase prediction accuracy for protein function prediction.ResultsIn this paper, we propose an effective Markov chain based CC algorithm (ICAM) to tackle the label deficiency problem in CC for interrelated proteins from PPI networks. Our idea is to model the problem using two distinct Markov chain classifiers to make separate predictions with regard to attribute features from protein data and relational features from relational information. The ICAM learning algorithm combines the results of the two classifiers to compute the ranks of labels to indicate the importance of a set of labels to an instance, and uses an ICA framework to iteratively refine the learning models for improving performance of protein function prediction from PPI networks in the paucity of labeled data.ConclusionExperimental results on the real-world Yeast protein-protein interaction datasets show that our proposed ICAM method is better than the other ICA-type methods given limited labeled training data. This approach can serve as a valuable tool for the study of protein function prediction from PPI networks.

【 授权许可】

Unknown   
© Wu et al.; licensee BioMed Central Ltd. 2014. 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.