Improving accuracy for cancer classification with a new algorithm for genes selection

BMC Bioinformatics

Methodology Article

Haiyan Wang¹  Zhijun Dai²  Zheming Yuan²  Hongyan Zhang³  Ming-shun Chen⁴ 

[1] Department of Statistics, Kansas State University, 66506, Manhattan, KS, USA;Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, 410128, Changsha, China;College of Bio-safety Science and Technology, Hunan Agricultural University, 410128, Changsha, China;Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, 410128, Changsha, China;College of Bio-safety Science and Technology, Hunan Agricultural University, 410128, Changsha, China;College of Information Science and Technology, Hunan Agricultural University, 410128, Changsha, China;USDA-ARS and Department of Entomology, Kansas State University, 66506, Manhattan, KS, USA;

关键词: Linear Discriminant Analysis;    Support Vector Machine Classifier;    Feature Subset;    Quadratic Discriminant Analysis;    Informative Gene;   

DOI  :  10.1186/1471-2105-13-298

 received in 2012-04-09, accepted in 2012-09-24,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundEven though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.ResultsWe applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature.ConclusionsEvaluation of a gene’s contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.

【 授权许可】

Unknown   
© Zhang et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.