基因表达数据的MOACO双聚类

Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen
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引用次数: 2

摘要

许多生物信息学数据集来自DNA微阵列实验。基因表达数据的双聚类可以识别在不同条件下具有相似行为的基因。蚁群优化算法已被证明是一种有效的问题解决策略,适用于广泛的问题领域。多目标蚁群优化(MOACO)主要研究多目标组合优化问题。本文将拥挤更新技术引入到MOACOB中,提出了拥挤MOACO双聚类算法,从基因表达数据中挖掘双聚类。在两个真实基因表达数据上给出了双聚类算法的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MOACO Biclustering of gene expression data
Many bioinformatics data sets come from DNA microarray experiments. Biclustering of gene expression data can identify genes with similar behaviour with respect to different conditions. Ant Colony Optimisation (ACO) algorithms have been shown to be effective problem solving strategies for a wide range of problem domains. Multiple Objective Ant Colony Optimisation (MOACO) mainly focuses on solving the multiple objective combinatorial optimisation problems. This paper incorporates crowding update technology into MOACOB and proposes crowding MOACO biclustering algorithm to mine biclusters from gene expression data. Experimental results are shown for biclustering algorithm on two real gene expression data.
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