Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권오란 | * |
dc.date.accessioned | 2018-11-30T16:30:12Z | - |
dc.date.available | 2018-11-30T16:30:12Z | - |
dc.date.issued | 2017 | * |
dc.identifier.isbn | 9781509016105 | * |
dc.identifier.other | OAK-23615 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/247147 | - |
dc.description.abstract | Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we compare the performance of both methods for real doubly repeated omics data from a crossover study. © 2016 IEEE. | * |
dc.language | English | * |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | * |
dc.subject | Crossover design | * |
dc.subject | Linear mixed effect model | * |
dc.subject | Repeated measurements | * |
dc.title | Analysis for doubly repeated omics data from crossover design | * |
dc.type | Conference Paper | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 1749 | * |
dc.relation.lastpage | 1752 | * |
dc.relation.journaltitle | Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 | * |
dc.identifier.doi | 10.1109/BIBM.2016.7822782 | * |
dc.identifier.scopusid | 2-s2.0-85013287716 | * |
dc.author.google | Choi S. | * |
dc.author.google | Park S.-Y. | * |
dc.author.google | Kim H. | * |
dc.author.google | Kwon O. | * |
dc.author.google | Park T. | * |
dc.contributor.scopusid | 권오란(55713470100) | * |
dc.date.modifydate | 20240123125010 | * |