Bayesian Neural Networks for Selection of Drug Sensitive Genes

Recent advances in high-throughput biotechnologies have provided an unprecedented opportunity for biomarker discovery, which, from a statistical point of view, can be cast as a variable selection problem. This problem is challenging due to the high-dimensional and nonlinear nature of omics data and,...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 113; no. 523; pp. 955 - 972
Main Authors:	Liang, Faming, Li, Qizhai, Zhou, Lei
Format:	Journal Article
Language:	English
Published:	United States Taylor & Francis 03.07.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Subjects:	Adaptive algorithms Algorithms antineoplastic agents Applications and Case Studies Approximation Bayesian analysis Bayesian theory Biological markers Biomarkers Biotechnology Cancer Cancer cell line encyclopedia cell lines Computation computers Consistency data collection Drugs Encyclopedias equations Feature selection Genes Identification Markov analysis Markov chain Markov chains Monte Carlo simulation neoplasm cells Networks Neural networks Nonlinear systems Nonlinear variable selection Omics data OpenMP Parallel Markov chain Monte Carlo Regression analysis Simulation Statistical methods Statistics Cancer Cell Line Encyclopedia Nonlinear Variable Selection OpenMP Omics Data Parallel Markov Chain Monte Carlo
ISSN:	0162-1459, 1537-274X, 1537-274X
Online Access:	Get full text
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