Improving classification of microarray data using prototype-based feature selection
ACM SIGKDD Explorations Newsletter
Improving gene selection in microarray data analysis using fuzzy patterns inside a CBR system
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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The advent of DNA microarray technology has supplied a large volume of data to many fields like machine learning and data mining. Intelligent support is essential for managing and interpreting this great amount of information. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. In this paper we demonstrate how a supervised fuzzy pattern algorithm can be used to perform DNA microarray data reduction over real data. The benefits of our method can be employed to find biologically significant insights relating to meaningful genes in order to improve previous successful techniques. Experimental results on acute myeloid leukemia diagnosis show the effectiveness of the proposed approach.