摘要
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.
| 源语言 | 英语 |
|---|---|
| 页(从-至) | 58-72 |
| 页数 | 15 |
| 期刊 | International Journal of Functional Informatics and Personalised Medicine |
| 卷 | 3 |
| 期 | 1 |
| DOI | |
| 出版状态 | 已出版 - 2010 |
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