NDLI: <formula formulatype=inline><tex Notation=TeX>$f$</tex></formula>-Information Measures for Efficient Selection of Discriminative Genes From Microarray Data

Content Provider	IEEE Xplore Digital Library
Author	Maji, P.
Copyright Year	1964
Abstract	Among the great amount of genes presented in microarray gene expression data, only a small fraction is effective for performing a certain diagnostic test. In this regard, mutual information has been shown to be successful for selecting a set of relevant and nonredundant genes from microarray data. However, information theory offers many more measures such as the f-information measures that may be suitable for selection of genes from microarray gene expression data. This paper presents different f-information measures as the evaluation criteria for gene selection problem. To compute the gene-gene redundancy (respectively, gene-class relevance), these information measures calculate the divergence of the joint distribution of two genes' expression values (respectively, the expression values of a gene and the class labels of samples) from the joint distribution when two genes (respectively, the gene and class label) are considered to be completely independent. The performance of different f-information measures is compared with that of the mutual information based on the predictive accuracy of naive Bayes classifier, K -nearest neighbor rule, and support vector machine. An important finding is that some f-information measures are shown to be effective for selecting relevant and nonredundant genes from microarray data. The effectiveness of different f-information measures, along with a comparison with mutual information, is demonstrated on breast cancer, leukemia, and colon cancer datasets. While some f -information measures provide 100% prediction accuracy for all three microarray datasets, mutual information attains this accuracy only for breast cancer dataset, and 98.6% and 93.6% for leukemia and colon cancer datasets, respectively.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	7
File Size	432481
Starting Page	1063
Ending Page	1069
File Format	PDF
ISSN	00189294
Volume Number	56
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mutual information Gene expression Accuracy Breast cancer Colon Performance evaluation Testing Information theory Genetic communication Distributed computing mutual information Classification feature selection gene selection microarray analysis
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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