基于子块矩阵马尔可夫聚类识别动态蛋白质相互作用网络功能模块  

Identifying Functional Modules in Dynamic Protein-protein Interaction Networks Using Subblock Matrix-based Markov Clustering

作　　者：张锦雄[1,2] 潘扬健 孟雪莉 唐伊红 巴依提力·努尔旦艾力 王鑫 左振文 陈清华 郭顶亮 韦冰冰 陈陆坤 ZHANG Jinxiong;PAN Yangjian;MENG Xueli;TANG Yihong;Bayitili NUERDANAILI;WANG Xin;ZUO Zhenwen;CHEN Qinghua;GUO Dingliang;WEI Bingbing;CHEN Lukun(School of Computer,Electronics and Information,Guangxi University,Nanning,530004;Laboratory of Parallel,Distributed and Intelligent Computing of Guangxi Universities and Colleges,Nanning,530004)

机构地区：[1]广西大学计算机与电子信息学院,南宁530004 [2]广西高校并行分布与智能计算重点实验室,南宁530004

出　　处：《基因组学与应用生物学》2024年第2期217-227,共11页Genomics and Applied Biology

基　　金：国家自然科学基金(62362004)资助。

摘　　要：细胞生物过程具有时序动态性,蛋白质功能模块是驱动细胞生物过程的功能单位。为了蛋白质功能模块识别,本文将细胞生物过程建模为动态时序表达相关蛋白质相互作用网络(DTEPIN);构建子块矩阵以表示动态时序表达相关蛋白质相互作用网络;利用子块矩阵特殊性,分析时空复杂度和并行性;优化设计马尔可夫聚类算法,以识别动态时序表达相关蛋白质相互作用网络中的蛋白质功能模块。为了支持基于子块矩阵马尔可夫聚类过程,本文运用图形处理器并行计算矩阵乘积。实验结果表明,与已有同类算法相比,所设计算法识别的蛋白质功能模块,统计匹配质量更高且精确匹配数量更多。Cellular biological processes are temporally dynamic,and protein functional modules are the functional units that drive cellular biological processes.In order to identify protein functional modules,cellular biological processes were modelled as dynamically and temporally gene expression-associated protein-protein interaction networks(DTEPIN).A sub-block matrix was constructed to represent DTEPIN.By employing the particularity of the sub-block matrix and analyzing time-space complexity and parallelism,Markov clustering algorithm was optimally designed to identify the protein functional modules in DTEPIN.In order to carry out the process of Markov clustering based on sub-block matrix,matrix multiplication using graphics processor unit was implemented to calculate matrix product in parallel.Experimental results show that compared with the existing similar algorithms,the designed algorithm can accurately identify more protein functional modules and identify more protein functional modules with higher quality.

关 键 词：蛋白质功能模块 蛋白质-蛋白质相互作用 动态时序表达 马尔可夫聚类 GPU并行计算 

分 类 号：Q811.4[生物学—生物工程]