Feature Selection and Gene Clustering from Gene Expression Data

Pabitra Mitra; Dwijesh Dutta Majumder

doi:10.1109/ICPR.2004.1334213

Pattern Recognition, International Conference on

Feature Selection and Gene Clustering from Gene Expression Data

Year: 2004, Pages: 343-346

DOI Bookmark: 10.1109/ICPR.2004.1334213

Authors

Pabitra Mitra, Indian Statistical Institute
Dwijesh Dutta Majumder, Indian Statistical Institute

Abstract

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 features/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.

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Feature Selection and Gene Clustering from Gene Expression Data

Authors

Abstract

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