CSpace  > 系统科学研究所
Bayesian Neural Networks for Selection of Drug Sensitive Genes
Liang, Faming1; Li, Qizhai2; Zhou, Lei3
2018
Source PublicationJOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN0162-1459
Volume113Issue:523Pages:955-972
AbstractRecent 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.
KeywordCancer cell line encyclopedia Nonlinear variable selection Omics data OpenMP Parallel Markov chain Monte Carlo
DOI10.1080/01621459.2017.1409122
Language英语
Funding ProjectNSF[DMS-1612924] ; NSF[DMS/NIGMS R01-GM117597]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000446710500001
PublisherAMER STATISTICAL ASSOC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31334
Collection系统科学研究所
Affiliation1.Purdue Univ, Dept Stat, W Lafayette, IN 47906 USA
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
3.Univ Florida, Dept Mol Genet & Microbiol, Gainesville, FL USA
Recommended Citation
GB/T 7714
Liang, Faming,Li, Qizhai,Zhou, Lei. Bayesian Neural Networks for Selection of Drug Sensitive Genes[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,2018,113(523):955-972.
APA Liang, Faming,Li, Qizhai,&Zhou, Lei.(2018).Bayesian Neural Networks for Selection of Drug Sensitive Genes.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,113(523),955-972.
MLA Liang, Faming,et al."Bayesian Neural Networks for Selection of Drug Sensitive Genes".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 113.523(2018):955-972.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liang, Faming]'s Articles
[Li, Qizhai]'s Articles
[Zhou, Lei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liang, Faming]'s Articles
[Li, Qizhai]'s Articles
[Zhou, Lei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liang, Faming]'s Articles
[Li, Qizhai]'s Articles
[Zhou, Lei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.