Estimation of joint directed acyclic graphs with lasso family for gene networks
DC Field | Value | Language |
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dc.contributor.author | Han, Sung Won | - |
dc.contributor.author | Park, Sunghoon | - |
dc.contributor.author | Zhong, Hua | - |
dc.contributor.author | Ryu, Eun-Seok | - |
dc.contributor.author | Wang, Pei | - |
dc.contributor.author | Jung, Sehee | - |
dc.contributor.author | Lim, Jayeon | - |
dc.contributor.author | Yoon, Jeewhan | - |
dc.contributor.author | Kim, SungHwan | - |
dc.date.accessioned | 2022-11-05T13:41:41Z | - |
dc.date.available | 2022-11-05T13:41:41Z | - |
dc.date.created | 2022-11-04 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0361-0918 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/144851 | - |
dc.description.abstract | Biological regulatory pathways provide important information for target gene cancer therapy. Frequently, estimating the gene networks of two distinct patient groups is a worthwhile investigation. This paper proposes an approach, called jDAG, to the estimation of directed joint networks. It can identify common directed edges with joint data sets and distinct edges. In a simulation study, we show that the proposed jDAG outperforms existing methods although it does require longer computational times. We also present and discuss the example study of a breast cancer data set with ER + and ER-. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS INC | - |
dc.subject | INVERSE COVARIANCE ESTIMATION | - |
dc.subject | ADAPTIVE LASSO | - |
dc.subject | BREAST-CANCER | - |
dc.subject | SELECTION | - |
dc.title | Estimation of joint directed acyclic graphs with lasso family for gene networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Jeewhan | - |
dc.identifier.doi | 10.1080/03610918.2019.1618869 | - |
dc.identifier.scopusid | 2-s2.0-85067571934 | - |
dc.identifier.wosid | 000474052000001 | - |
dc.identifier.bibliographicCitation | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.50, no.9, pp.2793 - 2807 | - |
dc.relation.isPartOf | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.title | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.citation.volume | 50 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 2793 | - |
dc.citation.endPage | 2807 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | INVERSE COVARIANCE ESTIMATION | - |
dc.subject.keywordPlus | ADAPTIVE LASSO | - |
dc.subject.keywordPlus | BREAST-CANCER | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordAuthor | Bayesian network | - |
dc.subject.keywordAuthor | Drug response network | - |
dc.subject.keywordAuthor | Lasso estimation | - |
dc.subject.keywordAuthor | Probabilistic graphical model | - |
dc.subject.keywordAuthor | Structure equation model | - |
dc.subject.keywordAuthor | Unknown natural ordering | - |
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