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Gene association study with SVM,MLP and cross-validation for the diagnosis of diseases

Progress in Natural Science:Materials International 2008年第6期18卷 741-750页

作者： Junying Zhanga,*,Shenling Liu a,Yue Wang b aSchool of Computer Science and Engineering,Xidian University,161 Postbox,Xi’an 710071,China bDepartment of Electrical and Computer Engineering,Virginia Polytechnic Institute and State University,Alexandria,VA 22314,USA School of Computer Science and Engineering Xidian University 161 Postbox Xi’an 710071 China Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Alexandria VA 22314 USA

Gene association study is one of the major challenges of biochip technology both for gene diagnosis where only a gene subset is responsible for some diseases,and for the treatment of the curse of dimensionality which occurs especially in DNA microarray datasets where there are more than thousands of genes and only a few number of experiments(samples).This paper presents a gene selection method by training linear support vector machine(SVM)/nonlinear MLP(multilayer perceptron)classifiers and testing them with cross-validation for finding a gene subset which is optimal/suboptimal for the diagnosis of binary/multiple disease *** are selected with linear SVM classifier for the diagnosis of each binary disease types pair and tested by leave-one-out cross-validation;then,genes in the gene subset initialized by the union of them are deleted one by one by removing the gene which brings the greatest decrease of the generalization power,for samples,on the gene subset after removal,where generalization is measured by training MLPs with leave-one-out and leave-four-out *** proposed method was tested with experiments on real DNA microarray MIT data and NCI *** result shows that it outperforms conventional SNR method in the separability of the data with expression levels on selected *** real DNA microarray MIT/NCI data,which is composed of 7129/2308 eective genes with only 72/64 labeled sam-ples belonging to 2/4 disease classes,only 11/6 genes are selected to be diagnostic *** selected genes are tested by the classification of samples on these genes with SVM/MLP with leave-one-out/both leave-one-out and leave-four-out *** result of no misclassification indicates that the selected genes can be really considered as diagnostic genes for the diagnosis of the corresponding diseases.

关键词： DNA microarray data Curse of dimensionality Gene selection diagnostic genes SVM MLP Cross-validation

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