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

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 sta...

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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
Online Access	Get full text
ISSN	1367-4803 1367-4811 1367-4811 1460-2059
DOI	10.1093/bioinformatics/btm036

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Abstract	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.
AbstractList	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. 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.eduSupplementary information: Supplementary materials, including a detailed review of both binary and multiclass SVMs, and complete experimental results, are available at Bioinformatics online. 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. 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.tuckatyale.edu Supplementary information: Supplementary materials, including a detailed review of both binary and multiclass SVMs, and complete experimental results, are available at Bioinformatics online. 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. 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.MOTIVATIONGiven 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.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.RESULTSIn 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. 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. 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.
Author	Zhou, Xin Tuck, David P.
Author_xml	– sequence: 1 givenname: Xin surname: Zhou fullname: Zhou, Xin organization: Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA – sequence: 2 givenname: David P. surname: Tuck fullname: Tuck, David P. organization: To whom correspondence should be addressed
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Keywords	Selection DNA chip Minimization Support vector machine Microarray Optimization Original document Gene Classification Recursive method Simultaneity Bioinformatics Comparative study
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Snippet	Motivation: Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data... 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... MOTIVATION: Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data...
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SubjectTerms	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
Title	MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data
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