Application of Data mining in Protein sequence Classification

TL;DR

This paper reviews existing protein sequence classification methods and introduces a new technique aimed at reducing computational costs and improving accuracy, implemented through a custom tool.

Contribution

It presents a novel classification model for protein sequences that enhances accuracy and reduces computational overhead compared to previous methods.

Findings

Improved classification accuracy with the new model

Reduced computational overhead in protein sequence classification

Effective implementation through a custom-designed tool

Abstract

Protein sequence classification involves feature selection for accurate classification. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP,Rough Set Classifier etc for accurate classification. This paper presents a review is with three different classification models such as neural network model, fuzzy ARTMAP model and Rough set classifier model. This is followed by a new technique for classifying protein sequences. The proposed model is typically implemented with an own designed tool and tries to reduce the computational overheads encountered by earlier approaches and increase the accuracy of classification