Stable Gene Selection from Microarray Data via Sample Weighting

Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctiv...

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Vydáno v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 9; číslo 1; s. 262 - 272
Hlavní autoři:	Lei Yu, Yue Han, Berens, M. E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2012 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Bioinformatics Cancer classification Computational Biology - methods Data Mining - methods Feature selection Gene expression gene expression microarray Gene Expression Profiling gene selection Genes Humans Models, Genetic Monte Carlo methods Neoplasms - genetics Neoplasms - metabolism Oligonucleotide Array Sequence Analysis - methods stability Stability analysis Studies Support Vector Machine Support vector machines Training
ISSN:	1545-5963, 1557-9964, 1557-9964
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Abstract	Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctively have high confidence in the result of a selection method that selects similar sets of genes under some variations to the samples. However, a common problem of existing feature selection methods for gene expression data is that the selected genes by the same method often vary significantly with sample variations. In this work, we propose a general framework of sample weighting to improve the stability of feature selection methods under sample variations. The framework first weights each sample in a given training set according to its influence to the estimation of feature relevance, and then provides the weighted training set to a feature selection method. We also develop an efficient margin-based sample weighting algorithm under this framework. Experiments on a set of microarray data sets show that the proposed algorithm significantly improves the stability of representative feature selection algorithms such as SVM-RFE and ReliefF, without sacrificing their classification performance. Moreover, the proposed algorithm also leads to more stable gene signatures than the state-of-the-art ensemble method, particularly for small signature sizes.
AbstractList	Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctively have high confidence in the result of a selection method that selects similar sets of genes under some variations to the samples. However, a common problem of existing feature selection methods for gene expression data is that the selected genes by the same method often vary significantly with sample variations. In this work, we propose a general framework of sample weighting to improve the stability of feature selection methods under sample variations. The framework first weights each sample in a given training set according to its influence to the estimation of feature relevance, and then provides the weighted training set to a feature selection method. We also develop an efficient margin-based sample weighting algorithm under this framework. Experiments on a set of microarray data sets show that the proposed algorithm significantly improves the stability of representative feature selection algorithms such as SVM-RFE and ReliefF, without sacrificing their classification performance. Moreover, the proposed algorithm also leads to more stable gene signatures than the state-of-the-art ensemble method, particularly for small signature sizes. Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctively have high confidence in the result of a selection method that selects similar sets of genes under some variations to the samples. However, a common problem of existing feature selection methods for gene expression data is that the selected genes by the same method often vary significantly with sample variations. In this work, we propose a general framework of sample weighting to improve the stability of feature selection methods under sample variations. The framework first weights each sample in a given training set according to its influence to the estimation of feature relevance, and then provides the weighted training set to a feature selection method. We also develop an efficient margin-based sample weighting algorithm under this framework. Experiments on a set of microarray data sets show that the proposed algorithm significantly improves the stability of representative feature selection algorithms such as SVM-RFE and ReliefF, without sacrificing their classification performance. Moreover, the proposed algorithm also leads to more stable gene signatures than the state-of-the-art ensemble method, particularly for small signature sizes.Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection method is the stability of the selected genes. Experts instinctively have high confidence in the result of a selection method that selects similar sets of genes under some variations to the samples. However, a common problem of existing feature selection methods for gene expression data is that the selected genes by the same method often vary significantly with sample variations. In this work, we propose a general framework of sample weighting to improve the stability of feature selection methods under sample variations. The framework first weights each sample in a given training set according to its influence to the estimation of feature relevance, and then provides the weighted training set to a feature selection method. We also develop an efficient margin-based sample weighting algorithm under this framework. Experiments on a set of microarray data sets show that the proposed algorithm significantly improves the stability of representative feature selection algorithms such as SVM-RFE and ReliefF, without sacrificing their classification performance. Moreover, the proposed algorithm also leads to more stable gene signatures than the state-of-the-art ensemble method, particularly for small signature sizes.
Author	Berens, M. E. Yue Han Lei Yu
Author_xml	– sequence: 1 surname: Lei Yu fullname: Lei Yu email: lyu@binghamton.edu organization: Dept. of Comput. Sci., State Univ. of New York, Binghamton, NY, USA – sequence: 2 surname: Yue Han fullname: Yue Han email: yhan@binghamton.edu organization: Dept. of Comput. Sci., State Univ. of New York, Binghamton, NY, USA – sequence: 3 givenname: M. E. surname: Berens fullname: Berens, M. E. email: mberens@tgen.org organization: Cancer & Cell Biol. Div., Translational Genomics Res. Inst., Phoenix, AZ, USA
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SubjectTerms	Algorithms Bioinformatics Cancer classification Computational Biology - methods Data Mining - methods Feature selection Gene expression gene expression microarray Gene Expression Profiling gene selection Genes Humans Models, Genetic Monte Carlo methods Neoplasms - genetics Neoplasms - metabolism Oligonucleotide Array Sequence Analysis - methods stability Stability analysis Studies Support Vector Machine Support vector machines Training
Title	Stable Gene Selection from Microarray Data via Sample Weighting
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