There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 218 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 211 | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 32 | - |
dc.contributor.author | Wang, Shuang | - |
dc.contributor.author | Zhang, Yuchen | - |
dc.contributor.author | Dai, Wenrui | - |
dc.contributor.author | Lauter, Kristin | - |
dc.contributor.author | Kim, Miran | - |
dc.contributor.author | Tang, Yuzhe | - |
dc.contributor.author | Xiong, Hongkai | - |
dc.contributor.author | Jiang, Xiaoqian | - |
dc.date.accessioned | 2023-12-22T00:11:36Z | - |
dc.date.available | 2023-12-22T00:11:36Z | - |
dc.date.created | 2020-09-08 | - |
dc.date.issued | 2016-01 | - |
dc.description.abstract | Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.32, no.2, pp.211 - 218 | - |
dc.identifier.doi | 10.1093/bioinformatics/btv563 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.scopusid | 2-s2.0-84959864390 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/48113 | - |
dc.identifier.url | https://academic.oup.com/bioinformatics/article/32/2/211/1744166 | - |
dc.identifier.wosid | 000368360100008 | - |
dc.language | 영어 | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title | HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | PRIVACY | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | EXOME | - |
dc.subject.keywordPlus | GENE | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.