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IMPROVEMENT IN ESTIMATION OF UNIDENTIFIED VALUE (GENE EXPRESSION DATA) IN BIOTECHNOLOGY

Baitali Nath, Bindu Agarwalla, Laxman Sahoo

In this era, DNA microarray technology is used combining with different data mining processes for extracting relevant knowledge from genes of organisms to discover the association between noble diseases and their correlated genes. However, this gene expression data frequently contains absent values which are to be dealt with to stop them from causing drastic affect in further analysis processes. To overcome the same, a number of missing-value recovery approaches are being introduced to serve the purpose. In this paper, a Clustering Approach of Collaborative Filtering is projected to estimate missing values more precisely than done by existing approaches. The Collaborative Filtering used in the process, which is primarily used in Recommender Systems, has been united with a basic clustering method based on Rough-Set Theory to impute a missing value.