Conference proceeding
Multi-Objective Evolutionary Algorithm for Mining 3D Clusters in Gene-sample-time Microarray Data
2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, pp 442-447
01 Jan 2008
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Latest microarroy technique can measure the expression levels of a set of genes under a set of samples during a series of time points, and generates new datasets which are called gene-sample-lime (simply GST) microarray data. Mining three-dimensional (3D) clusters from GST datasets is important in bioinformatics research and biomedical applications. Several objectives in conflict with each other have to be optimized simultaneously during mining 3D clusters, so multi-objective modeling is suitable for solving 3D clustering. This paper proposes a novel multi-objective evolutionary 3D clustering algorithm to mine 3D cluster in 3D microarray data. Experimental results on real dataset show that our approach can find significant 3D clusters of high quality.
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Details
- Title
- Multi-Objective Evolutionary Algorithm for Mining 3D Clusters in Gene-sample-time Microarray Data
- Creators
- Junwan Liu - National University of Defense TechnologyZhoujun Li - University of DefenceXiaohua Hu - Drexel University, Information Science (Informatics)Yiming Chen - National University of Defense Technology
- Publication Details
- 2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, pp 442-447
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics); Neurobiology and Anatomy
- Web of Science ID
- WOS:000263829500101
- Scopus ID
- 2-s2.0-57949100269
- Other Identifier
- 991019173552804721
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InCites Highlights
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods