Multi-Classifiers Comparison for Protein Secondary Structure Prediction

Abstract

Secondary structure prediction of protein is a crucial part while assessing proteins three dimensional structure. Amongst countless techniques created for forecasting proteins structural properties, novel hybrid classifiers and ensembles which predicts from numerous designs be publicized headed for improving the rate of accuracy. Here training, optimization has been done by using several classifiers like, AdaBoost Classifier, Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM) classifier for predicting protein secondary structure. The model validates to facilitate on the whole accuracy of each planned altogether classifier in order toward comparing them to get higher classification accuracy.