國立虎尾科技大學 |

Bayesian lasso : = An extension for genome-wide association study.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Bayesian lasso :/
Reminder of title:	An extension for genome-wide association study.
Author:	Joo, LiJin.
Description:	1 online resource (119 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 78-09, Section: B.
Contained By:	Dissertations Abstracts International78-09B.
Subject:	Biostatistics. -
Online resource:	click for full text (PQDT)
ISBN:	9781369630114

Bayesian lasso : = An extension for genome-wide association study.
Joo, LiJin.

Bayesian lasso :An extension for genome-wide association study. - 1 online resource (119 pages)

Source: Dissertations Abstracts International, Volume: 78-09, Section: B.

Thesis (Ph.D.)--New York University, 2017.

Includes bibliographical references

In genome-wide association study (GWAS), variable selection has been used for prioritizing candidate single-nucleotide polymorphism (SNP). Relating densely located SNPs to a complex trait, we need a method that is robust under various genetic architectures, yet is sensitive enough to detect the marginal difference between null and non-null factors. For this problem, ordinary Lasso produced too many false positives, and Bayesian Lasso by Gibbs samplers became too conservative when selection criterion was posterior credible sets. My proposals to improve Bayesian Lasso include two aspects: To use stochastic approximation, variational Bayes for increasing computational efficiency and to use a Dirichlet-Laplace prior for separating small effects from nulls better. Both a double exponential prior of Bayesian Lasso and a Dirichlet-Laplace prior have a global-local mixture representation, and variational Bayes can effectively handle the hierarchies of a model due to the mixture representation. In the analysis of simulated and real sequencing data, the proposed methods showed meaningful improvements on both efficiency and accuracy.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9781369630114Subjects--Topical Terms:

783654
Biostatistics.
Subjects--Index Terms:

BayesianIndex Terms--Genre/Form:

554714
Electronic books.

Bayesian lasso : = An extension for genome-wide association study.
LDR:02617ntm a22004337 4500 001 1144182
005 20240531083752.5
006 m o d
007 cr mn ---uuuuu
008 250605s2017 xx obm 000 0 eng d
020 $a 9781369630114
035 $a (MiAaPQ)AAI10243856
035 $a (MiAaPQ)nyu:12793
035 $a AAI10243856
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Joo, LiJin. $3 1469123
245 1 0 $a Bayesian lasso : $b An extension for genome-wide association study.
264 0 $c 2017
300 $a 1 online resource (119 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 78-09, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Oh, Cheongeun.
502 $a Thesis (Ph.D.)--New York University, 2017.
504 $a Includes bibliographical references
520 $a In genome-wide association study (GWAS), variable selection has been used for prioritizing candidate single-nucleotide polymorphism (SNP). Relating densely located SNPs to a complex trait, we need a method that is robust under various genetic architectures, yet is sensitive enough to detect the marginal difference between null and non-null factors. For this problem, ordinary Lasso produced too many false positives, and Bayesian Lasso by Gibbs samplers became too conservative when selection criterion was posterior credible sets. My proposals to improve Bayesian Lasso include two aspects: To use stochastic approximation, variational Bayes for increasing computational efficiency and to use a Dirichlet-Laplace prior for separating small effects from nulls better. Both a double exponential prior of Bayesian Lasso and a Dirichlet-Laplace prior have a global-local mixture representation, and variational Bayes can effectively handle the hierarchies of a model due to the mixture representation. In the analysis of simulated and real sequencing data, the proposed methods showed meaningful improvements on both efficiency and accuracy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Biostatistics. $3 783654
650 4 $a Genetics. $3 578972
650 4 $a Statistics. $3 556824
653 $a Bayesian
653 $a Ggenome-wide association study
653 $a Lasso
653 $a Shrinkage prior
653 $a Variable selection
653 $a Variational Bayes
655 7 $a Electronic books. $2 local $3 554714
690 $a 0308
690 $a 0369
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a New York University. $b Environmental Health Medicine. $3 1469124
773 0 $t Dissertations Abstracts International $g 78-09B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10243856 $z click for full text (PQDT)