Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권오란 | * |
dc.contributor.author | 김유진 | * |
dc.date.accessioned | 2020-08-31T16:30:09Z | - |
dc.date.available | 2020-08-31T16:30:09Z | - |
dc.date.issued | 2020 | * |
dc.identifier.issn | 1748-5673 | * |
dc.identifier.issn | 1748-5681 | * |
dc.identifier.other | OAK-27922 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/255305 | - |
dc.description.abstract | In a crossover design, individuals usually undergo all experimental conditions, and the measurements of biomarkers are repeatedly observed at serial time points for each experimental condition. To analyse time-dependent changing patterns of biomarkers, clustering algorithms are commonly used across time points to group together subjects having similar changing patterns. Among the clustering methods, hierarchical- and K-means clustering have been popularly used. However, since they are originally unsupervised approaches, they do not identify different changing patterns between experimental conditions. Therefore, we propose a new two-stage clustering method focusing on changing patterns. The first stage is to eliminate non-informative biomarkers using Euclidean distances, and the second stage is to allocate the remaining biomarkers to predefined patterns using a correlation-based distance. We demonstrate the advantages of our proposed method by simulation and real data analysis. | * |
dc.language | English | * |
dc.publisher | INDERSCIENCE ENTERPRISES LTD | * |
dc.subject | two-stage | * |
dc.subject | pattern clustering | * |
dc.subject | biomarker expression | * |
dc.subject | intervention study | * |
dc.subject | cross-over design | * |
dc.title | Two-stage clustering analysis to detect pattern change of biomarker expression between experimental conditions | * |
dc.type | Article | * |
dc.relation.issue | 4 | * |
dc.relation.volume | 23 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 299 | * |
dc.relation.lastpage | 317 | * |
dc.relation.journaltitle | INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | * |
dc.identifier.doi | 10.1504/IJDMB.2020.108701 | * |
dc.identifier.wosid | WOS:000556881700002 | * |
dc.author.google | Huh, Iksoo | * |
dc.author.google | Choi, Sunghoon | * |
dc.author.google | Kim, Youjin | * |
dc.author.google | Park, Soo-Yeon | * |
dc.author.google | Kwon, Oran | * |
dc.author.google | Park, Taesung | * |
dc.contributor.scopusid | 권오란(55713470100) | * |
dc.contributor.scopusid | 김유진(55766503300;57301428800;57207797446;57026608400) | * |
dc.date.modifydate | 20240123125010 | * |