Conference proceeding
Granular SVM-RFE gene selection algorithm for reliable prostate cancer classification on microarray expression data
BIBE 2005: 5TH IEEE SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, v 2005
01 Jan 2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Selecting the most informative cancer-related genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This paper presents the novel Granular Support Vector Machines - Recursive Feature Elimination (GSVM-RFE) algorithm for the gene selection task. As a biologically meaningful hybrid method of statistical learning theory and granular computing theory, GSVM-RFE can separately eliminate irrelevant, redundant or noisy genes in different granules at different stages and can select positively related genes, and negatively related genes in balance. Simulation results on the prostate cancer dataset show that GSVM-RFE is statistically much more accurate than traditional algorithms for the prostate cancer classification. More importantly, GSVM-RFE extracts a compact "perfect" gene subset of 17 genes with 100% accuracy. To our best knowledge, this is the first time such a "perfect" gene subset is reported, which is expected to be helpful for prostate cancer study.
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Details
- Title
- Granular SVM-RFE gene selection algorithm for reliable prostate cancer classification on microarray expression data
- Creators
- Y C Tang - Georgia State UniversityY Q Zhang - Georgia State UniversityZ Huang - Georgia State UniversityX H Hu - Drexel UniversityIEEE Computer Society
- Publication Details
- BIBE 2005: 5TH IEEE SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, v 2005
- Conference
- BIBE 2005: 5TH IEEE SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, 5th
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Center for Weight, Eating and Lifestyle Science (WELL) [Historical]
- Web of Science ID
- WOS:000234335200042
- Scopus ID
- 2-s2.0-33751165030
- Other Identifier
- 991019173548904721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Engineering, Biomedical