Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 14; no. 2; pp. 360 - 369
• Main Authors: Peng, Wei, Li, Min, Chen, Lu, Wang, Lusheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bioinformatics; Computational biology; Computational Biology - methods; Computer applications; Educational institutions; functional similarity; IEEE transactions; Legged locomotion; Mathematical models; Methods; Nodes; protein domain; Protein function prediction; Protein Interaction Mapping; Protein Interaction Maps - physiology; protein-protein interaction network; Proteins; Proteins - classification; Proteins - physiology; Random walk; Reproducibility of Results; Saccharomyces cerevisiae Proteins
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2015.2394314

With the gap between the sequence data and their functional annotations becomes increasing wider, many computational methods have been proposed to annotate functions for unknown proteins. However, designing effective methods to make good use of various biological resources is still a big challenge for researchers due to function diversity of proteins. In this work, we propose a new method named ThrRW, which takes several steps of random walking on three different biological networks: protein interaction network (PIN), domain co-occurrence network (DCN), and functional interrelationship network (FIN), respectively, so as to infer functional information from neighbors in the corresponding networks. With respect to the topological and structural differences of the three networks, the number of walking steps in the three networks will be different. In the course of working, the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment on S. cerevisiae data show that our method achieves better prediction performance not only than the methods that consider both PIN data and GO term similarities, but also than the methods using both PIN data and protein domain information, which verifies the effectiveness of our method on integrating multiple biological data sources.