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dc.contributor.authorZheng, Charles
dc.contributor.authorSchwartz, Scott
dc.contributor.authorChapkin, Robert S.
dc.contributor.authorCarroll, Raymond J.
dc.contributor.authorIvanov, Ivan
dc.date.accessioned2016-02-25T13:18:42Z
dc.date.available2016-02-25T13:18:42Z
dc.date.issued2013-12-18
dc.identifier.citationZheng C, Schwartz S, Chapkin RS, Carroll RJ, Ivanov I (2012) Feature selection for high-dimensional integrated data. Proceedings of the 2012 SIAM International Conference on Data Mining: 1141–1150. Available: http://dx.doi.org/10.1137/1.9781611972825.98.
dc.identifier.doi10.1137/1.9781611972825.98
dc.identifier.urihttp://hdl.handle.net/10754/598323
dc.description.abstractMotivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.
dc.description.sponsorshipWe are indebted to the Texas A& M Brazos Computing Cluster and Institute of Developmentaland Molecular Biology for access to computingresources, and to professors David B. Dahl,Mohsen Pourahmadi, and Joel Zinn for helpful discussions.The infant microarray-metagenomics data wasprovided courtesy of Sharon M. Donovan, of the Divisionof Nutritional Sciences, U. of Illinois, Urbana, IL.This publication is based in part on work supported byAward No. KUS-C1-016-04, made by King AbdullahUniversity of Science and Technology (KAUST).
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.titleFeature selection for high-dimensional integrated data
dc.typeBook Chapter
dc.identifier.journalProceedings of the 2012 SIAM International Conference on Data Mining
dc.contributor.institutionTexas A & M Dept. Statistics
dc.contributor.institutionTexas A &M Program in Integrative Nutrition & Complex Diseases, Center for Environmental & Rural Health
dc.contributor.institutionTexas A & M Dept. Veterinary Physiology and Pharmacology
kaust.grant.numberKUS-C1-016-04
dc.date.published-online2013-12-18
dc.date.published-print2012-04-26


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