MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

Citation data:

BMC bioinformatics, ISSN: 1471-2105, Vol: 10, Issue: 1, Page: 260

Publication Year:
2009
Usage 57
Abstract Views 39
Full Text Views 18
Captures 44
Readers 36
Exports-Saves 8
Citations 38
Citation Indexes 38
Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/7149
PMID:
19698124
DOI:
10.1186/1471-2105-10-260
PMCID:
PMC2743671
Author(s):
Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu
Publisher(s):
Springer Nature; BIOMED CENTRAL LTD
Tags:
Biochemistry, Genetics and Molecular Biology; Computer Science; Mathematics; Ensemble clustering; Geometric complexity; High-dimensional structures; K-means clustering; Microarray clusters; Original algorithms; Sample classification; Unsupervised clustering methods
article description
Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.