Projection Based Clustering of Gene Expression Data

被引：0

作者：

Tasoulis, Sotiris K. ^{[1
]}

Plagianakos, Vassilis P. ^{[1
]}

Tasoulis, Dimitris K. ^{[2
]}

机构：

[1] Univ Cent Greece, Dept Comp Sci & Biomed Informat, Papassiopoulou 2-4, Lamia 35100, Greece

[2] Imperial Coll London, Dept Mat, London SW7 2AZ, England

来源：

COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS | 2010年 / 6160卷

关键词：

Unsupervised Clustering; Cluster Analysis; Principal Component Analysis; Kernel Density Estimation; Bioinformatics; Gene Expression Analysis; CLASSIFICATION; PREDICTION;

D O I：

暂无

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The microarray DNA technologies have given researchers the ability to examine, discover and monitor thousands of genes in a single experiment. Nonetheless, the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis, mainly due to the very high data dimensionality. A particular class of clustering algorithms has been very successful in dealing with such data, utilising information driven by the Principal Component Analysis. In this paper, we investigate the application of recently proposed projection based hierarchical clustering algorithms on gene expression microarray data. The algorithms apart from identifying the clusters present in a data set also calculate their number and thus require no special knowledge about the data.

引用

页码：228 / +

页数：4

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