MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data

• Published in: Bioinformatics Vol. 23; no. 9; pp. 1106 - 1114
• Main Authors: Zhou, Xin, Tuck, David P.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.05.2007; Oxford Publishing Limited (England)
• Subjects: Algorithms; Artificial Intelligence; Bioinformatics; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated - methods; Software; Selection; DNA chip; Minimization; Support vector machine; Microarray; Optimization; Original document; Gene; Classification; Recursive method; Simultaneity; Bioinformatics; Comparative study
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btm036

Motivation: Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data analysis. Support Vector Machine—Recursive Feature Elimination (SVM-RFE) is one of a group of recently described algorithms which represent the stat-of-the-art for gene selection. Just like SVM itself, SVM-RFE was originally designed to solve binary gene selection problems. Several groups have extended SVM-RFE to solve multiclass problems using one-versus-all techniques. However, the genes selected from one binary gene selection problem may reduce the classification performance in other binary problems. Results: In the present study, we propose a family of four extensions to SVM-RFE (called MSVM-RFE) to solve the multiclass gene selection problem, based on different frameworks of multiclass SVMs. By simultaneously considering all classes during the gene selection stages, our proposed extensions identify genes leading to more accurate classification. Contact: david.tuck@yale.edu Supplementary information: Supplementary materials, including a detailed review of both binary and multiclass SVMs, and complete experimental results, are available at Bioinformatics online.