Feature selection and gene clustering from gene expression data

被引：4
|
作者：
Mitra, P [1 ]
Majumder, DD [1 ]
机构：
[1] Indian Stat Inst, Machine Intelligent Unit, Kolkata 700108, W Bengal, India
来源：
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 | 2004年
关键词：
microarray; maximal information compression index; cancer classification; representation entropy; data mining;
D O I：
10.1109/ICPR.2004.1334213
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this article we describe an algorithm for feature selection and gene clustering from high dimensional gene expression data. The method is based on measuring similarity between feat\ures/genes whereby redundancy therein is removed. This does not need any search and therefore is fast. A novel feature similarity measure, called maximum information compression index, is used. The feature selection algorithm also obtains gene clusters in a multiscale fashion. The superiority of the algorithm, in terms of speed and performance, is established on a real life molecular cancer classification dataset.
引用
收藏
页码：343 / 346
页数：4
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