Feature Selection and Classification of Protein CDS Using n-Block substring weighted Linear Model

It is more important to analysis of huge gemonics data in Bioinformatics. Here we present a novel datamining approach to predict structure and function using protein`s primnary structure only. We propose not also to develope n-Block substring search algorithm in reducing enormous search space effectively in relation to feature selection, but to formulate weighted linear algorithm in a prediction of structure and function of a protein using primary structure. And we show efficient in protein domain characterization and classification by calculation weight value in determining domain association in each selected substring, and also reveal that more efficient results are acquired through claculated model score result in an inference about degree of association with each CDS(coding sequence) in domain.