A hybrid LDA and genetic algorithm for gene selection and classification of microarray data

In supervised classification of Microarray data, gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy. This paper introduces a new embedded approach to this difficult task where a genetic algorithm (GA) is combined with Fisher's linear discriminant analysis (LDA). This LDA-based GA algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA's discriminant coefficients in its dedicated crossover and mutation operators. Computational experiments on seven public datasets show that under an unbiased experimental protocol, the proposed algorithm is able to reach high prediction accuracies with a small number of selected genes.

[1]  Xuefeng Bruce Ling,et al.  Multiclass cancer classification and biomarker discovery using GA-based algorithms , 2005, Bioinform..

[2]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[3]  Jack Y. Yang,et al.  Partial Least Squares Based Dimension Reduction with Gene Selection for Tumor Classification , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[4]  Tianzi Jiang,et al.  A combinational feature selection and ensemble neural network method for classification of gene expression data , 2004, BMC Bioinformatics.

[5]  Wei Du,et al.  Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines , 2003, FEBS letters.

[6]  Haesun Park,et al.  A comparison of generalized linear discriminant analysis algorithms , 2008, Pattern Recognit..

[7]  Chris H. Q. Ding,et al.  Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[8]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[9]  Jin-Kao Hao,et al.  Fuzzy Logic for Elimination of Redundant Information of Microarray Data , 2008, Genom. Proteom. Bioinform..

[10]  Shaoning Pang,et al.  Classification consistency analysis for bootstrapping gene selection , 2007, Neural Computing and Applications.

[11]  Yong Xu,et al.  Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[12]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Nir Friedman,et al.  Tissue classification with gene expression profiles , 2000, RECOMB '00.

[15]  D. Allison,et al.  Microarray data analysis: from disarray to consolidation and consensus , 2006, Nature Reviews Genetics.

[16]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[17]  Li Li,et al.  A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. , 2005, Genomics.

[18]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[19]  Kuanquan Wang,et al.  Informative Gene Selection and Tumor Classification by Null Space LDA for Microarray Data , 2007, ESCAPE.

[20]  Jieping Ye,et al.  Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems , 2005, J. Mach. Learn. Res..

[21]  Sunho Lee,et al.  Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data , 2008, Statistical methods in medical research.

[22]  Jin-Kao Hao,et al.  A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data , 2006, EvoWorkshops.

[23]  T. Poggio,et al.  Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.

[24]  S. Ramaswamy,et al.  Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. , 2002, Cancer research.

[25]  Edward R. Dougherty,et al.  Is cross-validation valid for small-sample microarray classification? , 2004, Bioinform..

[26]  Patrick Tan,et al.  Genetic algorithms applied to multi-class prediction for the analysis of gene expression data , 2003, Bioinform..

[27]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[28]  B. Efron.  Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation , 1983 .

[29]  Sung-Bae Cho,et al.  Cancer classification using ensemble of neural networks with multiple significant gene subsets , 2007, Applied Intelligence.

[30]  Elena Marchiori,et al.  Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data , 2005, EvoWorkshops.

[31]  Shutao Li,et al.  Gene selection using genetic algorithm and support vectors machines , 2008, Soft Comput..

[32]  Ying Liu,et al.  A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification , 2007, Cancer informatics.

[33]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[34]  Zexuan Zhu,et al.  Markov blanket-embedded genetic algorithm for gene selection , 2007, Pattern Recognit..

[35]  Sung-Bae Cho,et al.  Prediction of colon cancer using an evolutionary neural network , 2004, Neurocomputing.

[36]  Zhoujun Li,et al.  An Effective Gene Selection Method Based on Relevance Analysis and Discernibility Matrix , 2007, PAKDD.

[37]  Aik Choon Tan,et al.  Ensemble machine learning on gene expression data for cancer classification. , 2003, Applied bioinformatics.

[38]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[39]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[40]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[41]  D. Dai,et al.  Generalized Discriminant Analysis for Tumor Classification with Gene Expression Data , 2006, 2006 International Conference on Machine Learning and Cybernetics.