On Classifying Mappings Induced by Granular Structures
Transactions on Rough Sets IX
RSCTC'2010 discovery challenge: mining DNA microarray data for medical diagnosis and treatment
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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The DNA microarray exploration topic is a really important area of research. Comparing samples of tissues we can find genes, which are characteristic of particular research problems. A number of researchers involved in bioinformatics are attempting to find effective gene extraction methods and classifiers, in order to predict particular medical problems. Even if we do not consider the ontological sense of genes, it is possible by information technology methods to find genes which are the most significant for a given research problem. An exemplary application of DNA microarrays can be the ability to detect some illnesses, personal identification, or distinguishing features of some organisms. In this work we describe our most effective (in the global sense) gene extraction method based on experimental A statistics, called SAM5. Next, we use the granular classifier based on weighed voting, which proved the best among those recently studied by Polkowski, and Artiemjew - 8_v1_w4 algorithm.