Predicting co-complezed protein pairs based on communication model using diverse biological data
Protein-protein interactions play key role in many fundamental biological processes, and comprehensively identifying them represents a crucial step towards systematically defining their cellular roles. Machine learning techniques have been employed to predict protein-protein interactions. One of such approaches is Naive Bayes approach which assumes conditional independence between features. And such problems as suffering from the missing value problems or being prohibitively time-consuming prevent them from being applied to predict PPI as readily as NB. In this work, we frame predicting PPI as a communication problem, and we train a classifier based on channel model (CBCM) to discriminate between pairs of proteins that are co-complexed and pairs that are not. We theoretically demonstrate that NB can be unified into CBCM in certain condition and also experimentally validate that CBCM is an effective approach for predicting co-complexed protein pairs from integrating diverse biological data. Our study suggests that PPI prediction problem can be effectively solved from the point view of communication problem.
protein-protein interaction information theory integrating diverse biological data
Zhang kuan Zheng Hao-ran Yang Xiao-fei Han Si-yuan Hou Hui-chao Leng Tie-cheng Ding Ning
Department of Computer Science and Technology,University of Science and Technology of China Hefei,Ch Department of Computer Science and Technology,University of Science and Technology of China
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）