生物效应大数据评估聚类算法的并行优化  

Parallel optimization for clustering algorithm of large-scale biological effect evaluation

作　　者：彭绍亮 杨顺云[2] 孙哲 程敏霞 崔英博 王晓伟 李非[3] 伯晓晨[3] 廖湘科[2] PENG Shaoliang;YANG Shunyun;SUN Zhe;CHENG Minxia;CUl Yingbo;WANG Xiaowei;LI Fei;BO Xiaochen;LIAO Xiangke(College of Computer Science and Electronic Engineering ＆ National Supercomputer Centre in Changsha Hunan University, Changsha 410082, China;Department of Computer Science, National University of Defense Technology, Changsha 410073, China;Academy of Military Medical Sciences, Beijing 100850, China)

机构地区：[1]湖南大学信息科学与工程学院&国家超级计算长沙中心,湖南长沙410082 [2]国防科技大学计算机学院,湖南长沙410073 [3]中国人民解放军军事医学科学院,北京100850

出　　处：《大数据》2018年第3期24-36,共13页Big Data Research

基　　金：国家重点研发计划基金资助项目(No.2017YFB0202603;No.2017YFC1311003;No.2016YFC1302500;No.2016YFB0200400;No.2017YFB0202104);国家自然科学基金资助项目(No.61772543;No.U1435222;No.61625202;No.61272056);广东省科学技术厅基金资助项目(No.2016B090918122);化学生物传感与计量学国家重点实验室基金项目~~

摘　　要：生物效应评估通过测定和分析生物制剂刺激各种人体细胞后的数字化转录组反应,能够快速确定相关的检测标识物和治疗靶标。基于潜在生物制剂作用下的细胞反应大数据,推测突发生物效应模式。综合考虑了MPI、Open MP两级并行加速,移植优化了基因探针富集分析(GSEA)比对算法和聚类算法,使用不同的数据量和并行度验证了优化后算法潜在的良好可扩展性和快速处理海量生物信息数据的能力。The biological assessment, including matching algorithm, is realized by measuring and analyzing the human cells’ transcription reaction after stimulated by biological agents, to quickly determine the relevant detection markers and treatment targets. Similarly, the big data strategy was used to estimate the sudden biological effect model. MPI, Open MP two-level parallel acceleration was considered, transplantation and optimization of the GSEA alignment algorithm and clustering algorithm were used. The potential scalability and the ability of dealing with massive data by testing different scales of data and parallelisms were improved.

关 键 词：GSEA 聚类 MPI OPENMP 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]