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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Veröffentlicht in:	Journal of the American Statistical Association Jg. 113; H. 523; S. 955 - 972
Hauptverfasser:	Liang, Faming, Li, Qizhai, Zhou, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 03.07.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	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
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Abstract	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, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo algorithm, on the OpenMP platform that provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancer drug sensitivities based on the data collected in the cancer cell line encyclopedia study. Supplementary materials for this article are available online.
AbstractList	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 non-linear nature of omics data and, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo (MCMC) algorithm, on the OpenMP platform which provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study.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 non-linear nature of omics data and, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo (MCMC) algorithm, on the OpenMP platform which provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study. 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, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo algorithm, on the OpenMP platform that provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancer drug sensitivities based on the data collected in the cancer cell line encyclopedia study. Supplementary materials for this article are available online. 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, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo algorithm, on the OpenMP platform that provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancer drug sensitivities based on the data collected in the cancer cell line encyclopedia study. 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 non-linear nature of omics data and, in general, it suffers three difficulties: (i) an unknown functional form of the nonlinear system, (ii) variable selection consistency, and (iii) high-demanding computation. To circumvent the first difficulty, we employ a feed-forward neural network to approximate the unknown nonlinear function motivated by its universal approximation ability. To circumvent the second difficulty, we conduct structure selection for the neural network, which induces variable selection, by choosing appropriate prior distributions that lead to the consistency of variable selection. To circumvent the third difficulty, we implement the population stochastic approximation Monte Carlo algorithm, a parallel adaptive Markov Chain Monte Carlo (MCMC) algorithm, on the OpenMP platform which provides a linear speedup for the simulation with the number of cores of the computer. The numerical results indicate that the proposed method can work very well for identification of relevant variables for high-dimensional nonlinear systems. The proposed method is successfully applied to identification of the genes that are associated with anticancerdrug sensitivities based on the data collected in the cancer cell line encyclopedia (CCLE) study.
Author	Li, Qizhai Zhou, Lei Liang, Faming
Author_xml	– sequence: 1 givenname: Faming surname: Liang fullname: Liang, Faming email: fmliang@purdue.edu organization: Department of Statistics, Purdue University – sequence: 2 givenname: Qizhai surname: Li fullname: Li, Qizhai organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences – sequence: 3 givenname: Lei surname: Zhou fullname: Zhou, Lei organization: Department of Molecular Genetics & Microbiology, University of Florida
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Cites_doi	10.1371/journal.pone.0009024 10.1198/jasa.2011.tm10563 10.1080/10618600.2015.1089775 10.1007/BF00993164 10.1080/01621459.2014.984812 10.1016/j.csda.2013.03.027 10.1007/BF00116037 10.1038/nature11003 10.1111/j.1467-9868.2005.00503.x 10.1007/BF02551274 10.1186/bcr2596 10.1111/1467-9868.00346 10.1016/j.ygyno.2007.08.009 10.1186/bcr2635 10.1080/10618600.2017.1328364 10.1002/prot.22718 10.1111/rssb.12095 10.1093/biomet/asn036 10.1214/15-AOS1334 10.1093/biomet/asq017 10.1109/TPAMI.1984.4767596 10.1080/01621459.2012.695654 10.4161/cam.4.1.10973 10.1214/10-AOS792 10.1111/j.2517-6161.1996.tb02080.x 10.1007/s11222-005-4786-8 10.1186/1471-2407-14-334 10.1198/016214501753382273 10.1214/13-AOS1087 10.1080/01621459.2014.920256 10.1093/neuonc/now037 10.1093/biomet/82.4.711 10.1214/09-AOAS285 10.1214/12-AOAS590 10.1200/JCO.2010.28.9199 10.1063/1.1699114 10.1111/j.1467-9868.2008.00674.x 10.1017/S0016672310000662 10.1080/01621459.2012.761942 10.1198/016214508000000337 10.1214/009053604000000238 10.1016/0893-6080(89)90020-8 10.1080/01621459.2012.682536 10.1111/j.1467-9868.2009.00718.x 10.1093/biomet/asn034 10.1038/nature10983 10.1023/A:1010933404324 10.1016/j.econlet.2005.12.017 10.1016/0893-6080(89)90003-8 10.1186/1471-2156-12-87 10.1080/03610929008830400 10.1158/0008-5472.CAN-11-1780 10.1093/biomet/66.2.237 10.1016/j.molonc.2015.07.006 10.1073/pnas.1205943109 10.1093/jnci/95.12.851 10.1111/1467-9868.00353 10.1198/jasa.2011.ap09769 10.1080/01621459.1995.10476572 10.1093/biomet/57.1.97 10.1214/14-AOAS755 10.1239/aap/1418396243 10.1214/14-AOS1289 10.1214/009053607000000019 10.1198/jasa.2008.tm08516 10.1093/annonc/mdn739 10.1214/009053606000000722 10.1198/jcgs.2010.10039 10.1080/01621459.2014.960967
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Keywords	Cancer Cell Line Encyclopedia Nonlinear Variable Selection OpenMP Omics Data Parallel Markov Chain Monte Carlo
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References	cit0033 cit0077 cit0034 cit0078 cit0031 cit0075 cit0032 cit0076 cit0073 cit0030 cit0074 cit0071 cit0039 cit0037 cit0038 cit0035 cit0079 cit0036 cit0022 cit0066 cit0023 cit0067 cit0020 cit0064 cit0021 cit0065 cit0062 Liu G. (cit0050) 2010; 78 cit0063 cit0060 cit0061 Sun S. (cit0070) 2016 Raghu M. (cit0059) 2017; 70 cit0028 cit0029 cit0026 cit0027 cit0068 cit0025 cit0055 cit0012 cit0056 cit0053 cit0010 cit0054 cit0052 Liu H. (cit0051) 2009; 10 cit0019 cit0017 cit0018 cit0015 cit0016 cit0013 cit0057 Tibshirani R. (cit0072) 1996; 58 cit0014 cit0058 cit0044 cit0001 cit0045 cit0042 Carlin B. (cit0011) 1996 cit0043 cit0040 cit0041 Fan J. (cit0024) 2009; 10 Stouffer S. (cit0069) 1949 cit0008 cit0009 cit0006 cit0007 cit0004 cit0048 cit0005 cit0049 cit0002 cit0046 cit0047 cit0003
References_xml	– volume-title: Bayes and Empirical Bayes Methods for Data Analysis year: 1996 ident: cit0011 – ident: cit0055 doi: 10.1371/journal.pone.0009024 – ident: cit0077 doi: 10.1198/jasa.2011.tm10563 – ident: cit0052 doi: 10.1080/10618600.2015.1089775 – ident: cit0006 doi: 10.1007/BF00993164 – ident: cit0064 doi: 10.1080/01621459.2014.984812 – volume-title: The American Soldier, Vol. 1: Adjustment During Army Life year: 1949 ident: cit0069 – ident: cit0074 doi: 10.1016/j.csda.2013.03.027 – ident: cit0062 doi: 10.1007/BF00116037 – ident: cit0005 doi: 10.1038/nature11003 – ident: cit0079 doi: 10.1111/j.1467-9868.2005.00503.x – ident: cit0019 doi: 10.1007/BF02551274 – ident: cit0054 – ident: cit0039 doi: 10.1186/bcr2596 – volume: 10 year: 2009 ident: cit0051 publication-title: Journal of Machine Learning Research – ident: cit0068 doi: 10.1111/1467-9868.00346 – ident: cit0076 doi: 10.1016/j.ygyno.2007.08.009 – ident: cit0058 doi: 10.1186/bcr2635 – ident: cit0003 doi: 10.1080/10618600.2017.1328364 – volume: 78 start-page: 2170 year: 2010 ident: cit0050 publication-title: Protein doi: 10.1002/prot.22718 – ident: cit0065 doi: 10.1111/rssb.12095 – ident: cit0048 doi: 10.1093/biomet/asn036 – ident: cit0013 doi: 10.1214/15-AOS1334 – ident: cit0012 doi: 10.1093/biomet/asq017 – ident: cit0026 doi: 10.1109/TPAMI.1984.4767596 – ident: cit0045 doi: 10.1080/01621459.2012.695654 – ident: cit0042 doi: 10.4161/cam.4.1.10973 – ident: cit0063 doi: 10.1214/10-AOS792 – volume: 58 start-page: 267 year: 1996 ident: cit0072 publication-title: Journal of the Royal Statistical Society doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: cit0046 doi: 10.1007/s11222-005-4786-8 – start-page: 2066 year: 2016 ident: cit0070 publication-title: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence – ident: cit0031 doi: 10.1186/1471-2407-14-334 – ident: cit0021 doi: 10.1198/016214501753382273 – ident: cit0033 doi: 10.1214/13-AOS1087 – ident: cit0017 doi: 10.1080/01621459.2014.920256 – ident: cit0044 doi: 10.1093/neuonc/now037 – ident: cit0029 doi: 10.1093/biomet/82.4.711 – ident: cit0015 doi: 10.1214/09-AOAS285 – ident: cit0028 doi: 10.1214/12-AOAS590 – ident: cit0002 doi: 10.1200/JCO.2010.28.9199 – ident: cit0053 doi: 10.1063/1.1699114 – ident: cit0023 doi: 10.1111/j.1467-9868.2008.00674.x – ident: cit0022 doi: 10.1111/j.1467-9868.2008.00674.x – ident: cit0061 – ident: cit0056 doi: 10.1017/S0016672310000662 – ident: cit0047 doi: 10.1080/01621459.2012.761942 – ident: cit0057 doi: 10.1198/016214508000000337 – ident: cit0004 doi: 10.1214/009053604000000238 – ident: cit0036 doi: 10.1016/0893-6080(89)90020-8 – ident: cit0038 doi: 10.1080/01621459.2012.682536 – volume: 70 start-page: 2847 year: 2017 ident: cit0059 publication-title: Proceedings of the 34th International Conference on Machine Learning – ident: cit0060 doi: 10.1111/j.1467-9868.2009.00718.x – ident: cit0014 doi: 10.1093/biomet/asn034 – ident: cit0018 doi: 10.1038/nature10983 – ident: cit0009 doi: 10.1023/A:1010933404324 – ident: cit0040 doi: 10.1016/j.econlet.2005.12.017 – ident: cit0025 doi: 10.1016/0893-6080(89)90003-8 – ident: cit0027 doi: 10.1186/1471-2156-12-87 – ident: cit0034 doi: 10.1080/03610929008830400 – ident: cit0035 doi: 10.1158/0008-5472.CAN-11-1780 – ident: cit0001 doi: 10.1093/biomet/66.2.237 – ident: cit0020 doi: 10.1016/j.molonc.2015.07.006 – ident: cit0078 doi: 10.1073/pnas.1205943109 – ident: cit0030 doi: 10.1093/jnci/95.12.851 – ident: cit0067 doi: 10.1111/1467-9868.00353 – ident: cit0071 doi: 10.1198/jasa.2011.ap09769 – ident: cit0016 – volume: 10 start-page: 1829 year: 2009 ident: cit0024 publication-title: Journal of Machine Learning Research – ident: cit0041 doi: 10.1080/01621459.1995.10476572 – ident: cit0032 doi: 10.1093/biomet/57.1.97 – ident: cit0008 doi: 10.1214/14-AOAS755 – ident: cit0066 doi: 10.1239/aap/1418396243 – ident: cit0075 doi: 10.1214/14-AOS1289 – ident: cit0037 doi: 10.1214/009053607000000019 – ident: cit0073 doi: 10.1198/jasa.2008.tm08516 – ident: cit0010 doi: 10.1093/annonc/mdn739 – ident: cit0049 doi: 10.1214/009053606000000722 – ident: cit0043 doi: 10.1198/jcgs.2010.10039 – ident: cit0007 doi: 10.1080/01621459.2014.960967
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Snippet	Recent advances in high-throughput biotechnologies have provided an unprecedented opportunity for biomarker discovery, which, from a statistical point of view,...
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SubjectTerms	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
Title	Bayesian Neural Networks for Selection of Drug Sensitive Genes
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