Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data

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Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data

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dc.contributor.author Liu, Qingzhong
dc.contributor.author Sung, Andrew H.
dc.contributor.author Chen, Zhongxue
dc.contributor.author Liu, Jianzhong
dc.contributor.author Deng, Youping
dc.contributor.author Huang, Xudong
dc.date.accessioned 2011-11-22T16:23:01Z
dc.date.issued 2009
dc.identifier.citation Liu, Qingzhong, Andrew H. Sung, Zhongxue Chen, Jianzhong Liu, Xudong Huang, and Youping Deng. 2009. Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data. PLoS ONE 4(12): e8250. en_US
dc.identifier.issn 1932-6203 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:5350702
dc.description.abstract Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE) Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS) Gradient based Leave-one-out Gene Selection (GLGS) To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors. en_US
dc.language.iso en_US en_US
dc.publisher Public Library of Science en_US
dc.relation.isversionof doi:10.1371/journal.pone.0008250 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789385/pdf/ en_US
dash.license LAA
dc.subject computational biology en_US
dc.subject genomics en_US
dc.subject metagenomics en_US
dc.subject systems biology en_US
dc.subject computer science en_US
dc.subject applications en_US
dc.title Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal PLoS ONE en_US
dash.depositing.author Huang, Xudong
dc.date.available 2011-11-22T16:23:01Z
dash.affiliation.other HMS^Radiology-Brigham and Women's Hospital en_US

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