KAUST Grant NumberKUS-C1-016-04
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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.
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.
SponsorsWe 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).