J4 ›› 2014, Vol. 13 ›› Issue (12): 15-20.

• 数学与计算机科学 • 上一篇    下一篇

基于并行的非支配排序遗传Ⅱ算法优化双聚类

  

  1. 1.临沧师范高等专科学校数理系,云南临沧677000;2.福建农林大学计算机与信息学院,福州350002;
    3.临沧师范高等专科学校外语系,云南临沧677000
  • 收稿日期:2014-06-09 出版日期:2014-12-15 发布日期:2014-12-15
  • 作者简介:王丽美,助教,主要从事数据挖据、生物信息研究.

Optimization Biclustering Algorithm Based on Parallel Non-Dominated
Sorting Genetic AlgorithmⅡ

  1. 1.Department of Mathematics and Physics, Lincang Teachers' College, Lincang, Yunnan 677000, China; 2.College of Computer and
    Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China; 3.Department of Foreign Languages, Lincang
    Teachers' College, Lincang, Yunnan 677000, China
  • Received:2014-06-09 Online:2014-12-15 Published:2014-12-15

摘要:

双聚类是微阵列基因表达数据分析中很实用的一种数据挖掘技术,它是一种同时对微阵列基因和条件进行聚类的方
法,用来挖掘基因子集在条件子集下所体现出来的生物模式。传统的双聚类算法对于庞大的基因表达数据处理效率很弱,考
虑在jMetal平台上实现基因表达数据的双聚类的一种新的研究方法及思路。同时考虑加入并行策略,提高算法的效率。在酵
母啤酒细胞基因表达集和人类B-细胞两个标准数据集上对两个算法进行实验验证,表明所提出算法比其他多目标双聚类算
法呈现出更好的优越性。

关键词: 基因表达数据, 双聚类jMetal, 并行算法, 遗传算法

Abstract:

Biclustering is a very practical data mining technique in microarray gene expression data analysis and it is a way to cluster
both microarray genes and conditions simultaneously, which is used to excavate the biological mode reflected by the gene subset set
under the condition subset. The processing efficiency of traditional biclustering algorithm for large gene expression data is low, so this
paper explores a new research method and idea, i.e. applying gene expression data biclustering on jMetal platform. Also the parallel
strategy is proposed to improve the efficiency of the algorithm. Experiments on two datasets, yeast cell dataset and human B-cell
dataset, show that our approach exhibits better and more stable performance than other multi-objective biclustering algorithms.

Key words: gene expression data, biclustering jMetal, parallel algorithm, genetic algorithm

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