Incipient knowledge in protein folding kinetics states prophecy using deep neural network-based ensemble classifier

In this paper, we focus on incipient knowledge in the prediction of protein folding kinetics states using deep neural network-based stacking technique in ensemble classifier. Protein folding procedure is highly crucial for deciding the molecular function. The protein folding kinetic states check whether particle stimulus structure has done with the intermediary or not. Folding structure can be done with the stable intermediary (3S/3States) and without stable intermediary (2S/2State). Furthermore, there is a vast number of proteins in PDB still unfolding mechanism are found unknown. In this paper, we proposed stacking with the deep neural network for predicting protein folding kinetics states. In first level learning, we have used five bases classifier, i.e., naive Bayesian, decision tree, random forest, support vector machine and neural network and in the second level meta-learning we have used the rule-based method and deep neural network-based stacking in ensemble classifier for increasing the accuracy.