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

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dc.contributor.author Zhang, Hongyan
dc.contributor.author Wang, Haiyan
dc.contributor.author Dai, Zhijun
dc.contributor.author Chen, Ming-Shun
dc.contributor.author Yuan, Zheming
dc.date.accessioned 2013-04-02T17:02:49Z
dc.date.available 2013-04-02T17:02:49Z
dc.date.issued 2013-04-02
dc.identifier.uri http://hdl.handle.net/2097/15443
dc.description.abstract Background: Even 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. Results: We 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. Conclusions: Evaluation 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. en_US
dc.language.iso en_US en_US
dc.relation.uri http://www.biomedcentral.com/1471-2105/13/298 en_US
dc.subject Cancer classification en_US
dc.subject Gene expression en_US
dc.subject Binary Matrix Shuffling Filter (BMSF) en_US
dc.subject Support Vector Machine (SVM) en_US
dc.title Improving accuracy for cancer classification with a new algorithm for genes selection en_US
dc.type Article (publisher version) en_US
dc.date.published 2012 en_US
dc.citation.doi doi:10.1186/1471-2105-13-298 en_US
dc.citation.jtitle BMC Bioinformatics en_US
dc.citation.spage 298 en_US
dc.citation.volume 13 en_US
dc.contributor.authoreid hwang en_US
dc.contributor.authoreid mchen en_US


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