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f-Information Measures for Selection of Discriminative Genes from Microarray Data

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Scalable Pattern Recognition Algorithms

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Abstract

Microarray technology is one of the important biotechnological means that allows to record the expression levels of thousands of genes simultaneously within a number of different samples. An important application of microarray gene expression data in functional genomics is to classify samples according to their gene expression profiles. Among the large amount of genes present in microarray gene expression data, only a small fraction of them 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 chapter presents different f-information measures as the evaluation criteria for gene selection problem. The performance of different f-information measures is compared with that of 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 several cancer data sets.

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  1. Indian Statistical Institute, Kolkata, West Bengal, India

    Pradipta Maji & Sushmita Paul

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Correspondence to Pradipta Maji .

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Maji, P., Paul, S. (2014). f-Information Measures for Selection of Discriminative Genes from Microarray Data. In: Scalable Pattern Recognition Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-05630-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-05630-2_5

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