Journal article
MOACO Biclustering of gene expression data
International journal of functional informatics and personalised medicine, v 3(1), pp 58-72
01 Jan 2010
Abstract
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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Details
- Title
- MOACO Biclustering of gene expression data
- Creators
- Junwan Liu - 1 School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410073, PR ChinaZhoujun Li - 2 School of Computer Science and Engineering, Beihang University, Beijing 100191, PR ChinaXiaohua Hu - 3 College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USAYiming Chen - 4 School of Computer, National University of Deference Technology, Changsha 410073, PR China
- Publication Details
- International journal of functional informatics and personalised medicine, v 3(1), pp 58-72
- Publisher
- Inderscience Publishers
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-84855918710
- Other Identifier
- 991014878390904721