A generative model of identifying informative proteins from dynamic PPI networks  

作　　者：ZHANG Yuan CHENG Yue JIA KeBin ZHANG AiDong 

机构地区：[1]Department of Electrical Information and Control Engineering, Beijing University of Technology [2]Department of Computer Science and Engineering, State University of New York at Buffalo,Buffalo, NY 14260-2500, USA

出　　处：《Science China(Life Sciences)》2014年第11期1080-1089,共10页中国科学（生命科学英文版）

基　　金：supported by National Natural Science Foundation of China(30970780);Ph.D.Programs Foundation of Ministry of Education of China(20091103110005);the Project for the Innovation Team of Beijing,National Natural Science Foundation of China(81370038);the Beijing Natural Science Foundation(7142012);the Science and Technology Project of Beijing Municipal Education Commission(km201410005003);the Rixin Fund of Beijing University of Technology(2013-RX-L04);the Basic Research Fund of Beijing University of Technology

摘　　要：Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological processes.In this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins.Experiments were implemented on data of yeast cell cycles and different prostate cancer stages.We analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline methods.Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.Informative proteins are the proteins that play critical functional roles inside cells. They are the fundamental knowledge of translating bioinformatics into clinical practices. Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary, without considering the dynamics characteristics of biological processes. In this paper, we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks （PPINs）. In this model, the common representation of multiple PPINs is learned using a deep feature generation model, based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins. Experiments were implemented on data of yeast cell cycles and different prostate cancer stages. We analyze the effectiveness of reconstruction by comparing different methods, and the ranking results of informative proteins were also compared with the results from the baseline methods. Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.

关 键 词：dynamic protein-protein interaction network abnormal detection multi-view data deep belief network 

分 类 号：Q51[生物学—生物化学] Q811.4