An Approach to Enhance the Design of Protein Sequence Classifier Using Data Mining

Abstract

Analysis of protein plays a major role in bio-informatics domain. Protein sequence classification is most vital in this analysis of protein. In this scenario, different researchers proposed various techniques to classify protein sequence which is reviewed here at first. Different features extraction procedure from the protein sequence is a common part of all proposed classification model. But selection and arrangement of features are the most challenging task for classifying unknown protein sequences into its known families with high accuracy and preferable computational time. Analysis of recent trend points that traditional database system fails to classify a large amount of biological data which is overcome by data mining approach. New classification model involving four phases has been proposed here for classifying the unknown sequences into its family, generating knowledge-based as well as data analysis procedure. Here a hybrid algorithm with the combination of Fuzzy ARTMAP and neural network based algorithm is used. Neighbourhood analysis is also done in the final stage. This proposed model has implemented and validated with 497 different test sequences. Finally, it is also compared with the previous model and produce high accuracy level with low computation time than previous.