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

Shaoliang PENG; Shunyun YANG; Zhe SUN; Minxia CHENG; Yingbo CUI; Xiaowei WANG; Fei LI; Xiaochen BO; Xiangke LIAO

doi:10.11959/j.issn.2096-0271.2018027

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

TOPIC：BIOMEDICAL BIG DATA | 更新时间：2024-06-03

- Parallel optimization for clustering algorithm of large-scale biological effect evaluation
- Big Data Research Vol. 4, Issue 3, Pages: 2018027(2018)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2018027
  CLC： TP391
- Online First：2018-05，
  
  Published：15 May 2018
- 稿件说明：
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Shaoliang PENG, Shunyun YANG, Zhe SUN, et al. Parallel optimization for clustering algorithm of large-scale biological effect evaluation[J]. Big Data Research, 2018, 4(3): 2018027.
DOI：

Shaoliang PENG, Shunyun YANG, Zhe SUN, et al. Parallel optimization for clustering algorithm of large-scale biological effect evaluation[J]. Big Data Research, 2018, 4(3): 2018027. DOI： 10.11959/j.issn.2096-0271.2018027.

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、OpenMP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。

Abstract

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

OpenMP 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.

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Keywords

references

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