title: A NEW CLUSTERING METHOD AND ITS APPLICATION TO PROTEOMIC PROFILING FOR COLON CANCER.
abstract: In this paper, we introduce a new clustering method: quasi-clique merger, and its associated data pretreatment programs. This program constructs non-binary hierarchical trees with much smaller number of clusters in the outputs. And overlapping clusters are also allowed in the outputs. We applied this new method to cluster 60 human cancer cell lines (the NCI-60) using the previously identified proteomic determinants for chemosensitivity of 5-Fluorouracil (5-FU). All colon cancer cell lines were aggregated into a single cluster, indicating that the eight proteomic markers are potential diagnostic markers of colon cancer. The results based on the new clustering method have surpassed those based on previous methods on the same datasets.
authors.affiliation: Department of Mathematics, West Virginia University, Morgantown, WV 26506-6310.
authors.forename: Yongbin
authors.initials: Y
authors.lastname: Ou
authors.affiliation: MBR Cancer Center/Department of Community Medicine, West Virginia University, Morgantown, WV 26506-9300.
authors.forename: Lan
authors.initials: L
authors.lastname: Guo
authors.affiliation: Department of Mathematics, West Virginia University, Morgantown, WV 26506-6310.
authors.forename: Cun-Quan
authors.initials: CQ
authors.lastname: Zhang
keywords: Biological Data Mining; Microarray Data Analysis; NCI-60; chemosensitivity determinants; overlapping clusters; unsupervised hierarchical clustering