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生物效应大数据评估聚类算法的并行优化 被引量:2

Parallel optimization for clustering algorithm of large-scale biological effect evaluation
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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.
作者 彭绍亮 杨顺云 孙哲 程敏霞 崔英博 王晓伟 李非 伯晓晨 廖湘科 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)
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~~
关键词 GSEA 聚类 MPI OPENMP GSEA clustering MPI OpenMP
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  • 1Dinu I, Potter JD. Improving gene set analysis of microarray data by SAM-GS[J]. BMC Bioinforrnatics, 2007, 8(242): 1-13.
  • 2Tian L, Greenberg SA, Kong SW, et al. Discovering statistically significant pathways in expression profiling studies [J]. Proc Natl Acad Sci USA, 2005, 102:13544-13549.
  • 3Nam D, Kim SY: Gene-set approach for expression pattemanalysis[J]. BriefBioinform, 2008, May, 9(3): 189-97.
  • 4Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles[J]. Proc Natl Acad Sci USA, 2005, 102:15545-50.
  • 5Kim SY, Volsky DJ. PAGE: parametric analysis of gene set enrichment[J]. BMC Bioinformatics, 2005, 6:144.
  • 6Newton MA, Quintana FA, Den Boon JA, et al. Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis [J]. Ann Appl Star, 2007, 1:85-106.
  • 7Weijun L, Michael SF, Kerby S, et al. GAGE: generally applicable gene set enrichment for pathway analysis [J]. BMC Bioinformatics, 2009, 10:161.
  • 8Khatri P, Draghici S, Ostermeier GC, et al Profiling gene expression using onto-express[J]. Genomics, 2002, 79(2): 266-270.
  • 9Goeman JJ, Bhlmarm P: Analyzing gene expression data in terms of gene set: methodological issues [J]. Bioinformatics, 2007, 23 (8): 980-987.
  • 10Efron B, Tibshirani R. On testing the significance of sets of genes[J].Ann Appl Statist, 2007, 1(1): 107-129.

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