Stochastic block coordinate Frank-Wolfe algorithm for large-scale biological network alignment

作     者：Wang, Yijie Qian, Xiaoning 

作者机构：Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA 

出 版 物：《EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY》 (Eurasip J. Bioinform. Syst. Biol.)

年 卷 期：2016年第2016卷第1期

页      面：1页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 

基　　金：National Science Foundation [1447235, 1244068] National Institute Of Diabetes And Digestive And Kidney Diseases, National Institutes of Health [R21DK092845] Direct For Computer & Info Scie & Enginr Division of Computing and Communication Foundations Funding Source: National Science Foundation 

主　　题：Network alignment IsoRank Stochastic optimizatoin Frank-Wolfe algorithm 

摘      要：With increasingly big data available in biomedical research, deriving accurate and reproducible biology knowledge from such big data imposes enormous computational challenges. In this paper, motivated by recently developed stochastic block coordinate algorithms, we propose a highly scalable randomized block coordinate Frank-Wolfe algorithm for convex optimization with general compact convex constraints, which has diverse applications in analyzing biomedical data for better understanding cellular and disease mechanisms. We focus on implementing the derived stochastic block coordinate algorithm to align protein-protein interaction networks for identifying conserved functional pathways based on the IsoRank framework. Our derived stochastic block coordinate Frank-Wolfe (SBCFW) algorithm has the convergence guarantee and naturally leads to the decreased computational cost (time and space) for each iteration. Our experiments for querying conserved functional protein complexes in yeast networks confirm the effectiveness of this technique for analyzing large-scale biological networks.