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dc.contributor.authorLiu, Qingzhong
dc.contributor.authorSung, Andrew H
dc.contributor.authorChen, Zhongxue
dc.contributor.authorLiu, Jianzhong
dc.contributor.authorChen, Lei
dc.contributor.authorQiao, Mengyu
dc.contributor.authorWang, Zhaohui
dc.contributor.authorDeng, Youping
dc.contributor.authorHuang, Xudong
dc.date.accessioned2013-02-21T18:34:13Z
dc.date.issued2011
dc.identifier.citationLiu, Qingzhong, Andrew H. Sung, Zhongxue Chen, Jianzhong Liu, Lei Chen, Mengyu Qiao, Zhaohui Wang, Xudong Huang, and Youping Deng. 2011. Gene selection and classification for cancer microarray data based on machine learning and similarity measures. BMC Genomics 12(Suppl. 5): S1.en_US
dc.identifier.issn1471-2164en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10318285
dc.description.abstractBackground: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. Results: To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. Conclusions: On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi:10.1186/1471-2164-12-S5-S1en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287491/pdf/en_US
dash.licenseLAA
dc.titleGene Selection and Classification for Cancer Microarray Data Based on Machine Learning and Similarity Measuresen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Genomicsen_US
dash.depositing.authorHuang, Xudong
dc.date.available2013-02-21T18:34:13Z
dc.identifier.doi10.1186/1471-2164-12-S5-S1*
dash.authorsorderedfalse
dash.contributor.affiliatedHuang, Xudong


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