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Scalable Pattern Recognition Algorithms pp 225–252Cite as

Mutual Information Based Supervised Attribute Clustering for Microarray Sample Classification

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Abstract

In functional genomics, an important application of microarray data is to classify samples according to their gene expression profiles such as to classify cancer versus normal samples or to classify different types or subtypes of cancer. Hence, one of the major tasks with the gene expression data is to find groups of co-regulated genes whose collective expression is strongly associated with the sample categories or response variables. In this regard, a supervised gene clustering algorithm is presented in this chapter to find groups of genes. It directly incorporates the information of sample categories into the gene clustering process. A new quantitative measure, based on mutual information, is reported that incorporates the information of sample categories to measure the similarity between attributes. The supervised gene clustering algorithm is based on measuring the similarity between genes using the new quantitative measure. The performance of the new algorithm is compared with that of existing supervised and unsupervised gene clustering and gene selection algorithms based on the class separability index and the predictive accuracy of naive Bayes classifier, k-nearest neighbor rule, and support vector machine on several cancer and arthritis microarray data sets. The biological significance of the generated clusters is interpreted using the gene ontology.

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Authors and Affiliations

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

    Pradipta Maji & Sushmita Paul

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  1. Pradipta Maji
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  2. Sushmita Paul
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Correspondence to Pradipta Maji .

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Maji, P., Paul, S. (2014). Mutual Information Based Supervised Attribute Clustering for Microarray Sample Classification. In: Scalable Pattern Recognition Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-05630-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-05630-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05629-6

  • Online ISBN: 978-3-319-05630-2

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