Evolutionary Rough Feature Selection in Gene Expression Data

Evolutionary Rough Feature Selection in Gene Expression...

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Evolutionary Rough Feature Selection in Gene Expression Data By: Mohua Banerjee, Sushmita Mitra and Haider Banka This paper was basically written to tell how Rough Set Theory is useful in discerning between all objects –redundant or non redundant in a multiobjective framework and demonstrating three cancer examples to show the effectiveness of the algorithm. Authors start the paper discussing about computational molecular biology that is as similar and diverse like biology,computerscience,information technology,physics,chemistry,and statstics. One needs to analysze vast amount of data that are available in information science of computational molecular biology which are very large in amount around 24000-30000 human genes.Data mining is one of the techniques that can be used to achieve this. Further they say that Microarray data is useful while investigating complex interactions within the cell and microarray technologies have been utilized to evaluate the level of expression of thousands of genes in colon, breast as well as clustering. As it is very
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