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Two-stage clustering analysis to detect pattern change of biomarker expression between experimental conditions
- Title
- Two-stage clustering analysis to detect pattern change of biomarker expression between experimental conditions
- Authors
- Huh, Iksoo; Choi, Sunghoon; Kim, Youjin; Park, Soo-Yeon; Kwon, Oran; Park, Taesung
- Ewha Authors
- 권오란; 김유진
- SCOPUS Author ID
- 권오란; 김유진
- Issue Date
- 2020
- Journal Title
- INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
- ISSN
- 1748-5673
1748-5681
- Citation
- INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS vol. 23, no. 4, pp. 299 - 317
- Keywords
- two-stage; pattern clustering; biomarker expression; intervention study; cross-over design
- Publisher
- INDERSCIENCE ENTERPRISES LTD
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- 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.
- DOI
- 10.1504/IJDMB.2020.108701
- Appears in Collections:
- 신산업융합대학 > 식품영양학과 > Journal papers
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