Mutual Information-Based Supervised Attribute Clustering for Large Microarray Sample Classification

This paper investigates the application of the mutual information criterion to evaluate a set of attributes and to select an informative subset to be used as input data for microarray classification. A microarray is a multiplex lab-on-a-chip. It is a 2D array on a solid substrate, only a small fraction is effective for performing a certain task. 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 new supervised attribute clustering algorithm is proposed to find such groups of genes. It directly incorporates the information of sample categories into the attribute clustering process. A new quantitative measure, based on mutual information, is introduced which incorporates the information of sample categories to measure the similarity between attributes. This similarity measure is useful for reducing the redundancy among the attributes. This Clustering algorithm is more effective for analyzing biologically gene clusters with excellent predictive capability. Then, Fuzzy Classification Algorithm is applied to classify the selected gene set. Also, the proposed algorithm avoids the noise sensitivity problem of existing supervised gene clustering algorithms.