A 2-phased approach for detecting multiple loci associations with traits
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sunwon | - |
dc.contributor.author | Kang, Jaewoo | - |
dc.contributor.author | Oh, Junho | - |
dc.date.accessioned | 2021-09-06T23:52:58Z | - |
dc.date.available | 2021-09-06T23:52:58Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 1748-5673 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/109307 | - |
dc.description.abstract | The recent advance in SNP genotyping has made a significant contribution to reduction of the costs for large-scale genotyping. The development also has dramatically increased the size of the SNP genotype data. The increase in the volume of the data, however, has posed a huge obstacle to the conventional analysis techniques that are typically vulnerable to the high-dimensionality problem. To address the issue, we propose a method that exploits two well-tested models: the document-term model and the transaction analysis model. The proposed method consists of two phases. In the first phase, we reduce the dimensions of the SNP genotype data by extracting significant SNPs through transformation of the data in lieu of the document-term model. In the second phase, we discover the association rules that signify the relations between the SNPs and the traits, through the application of transactional analysis in the reduced-dimension genotype data. We validated the discovered rules through literature survey. Experiments were also carried out using the HGDP panel data provided by the Foundation Jean Dausset-CEPH, which prove the validity of our new method for identifying appropriate dimensional reduction and associations of multiple SNPs and traits. This paper is an extended version of our workshop paper presented in the 2010 International Workshop on Data Mining for High Throughput Data from Genome-Wide Association Studies. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | INDERSCIENCE ENTERPRISES LTD | - |
dc.subject | GENOME-WIDE ASSOCIATION | - |
dc.subject | INFERENCE | - |
dc.subject | SNPS | - |
dc.title | A 2-phased approach for detecting multiple loci associations with traits | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Jaewoo | - |
dc.identifier.doi | 10.1504/IJDMB.2012.049318 | - |
dc.identifier.scopusid | 2-s2.0-84867051665 | - |
dc.identifier.wosid | 000309610700007 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, v.6, no.5, pp.535 - 556 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | - |
dc.citation.volume | 6 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 535 | - |
dc.citation.endPage | 556 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | GENOME-WIDE ASSOCIATION | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | SNPS | - |
dc.subject.keywordAuthor | TF-IDF | - |
dc.subject.keywordAuthor | term frequency - inverse document frequency | - |
dc.subject.keywordAuthor | class association rule mining | - |
dc.subject.keywordAuthor | GWAS | - |
dc.subject.keywordAuthor | SNP | - |
dc.subject.keywordAuthor | bioinformatics | - |
dc.subject.keywordAuthor | apriori algorithm | - |
dc.subject.keywordAuthor | data mining | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.