Predicting protein secondary structure based on ensemble Neural Network

Protein structure prediction is very vital to innovative process of discovering new medications based on the knowledge of a biological target. It is also useful for scientifically exposing the biological basis of convoluted diseases and drug effects. Despite its usefulness, protein structure is very complex, thereby making its prediction to be arduous, timewasting and costly. These drawbacks necessitated the need to develop more effective techniques with high prediction capability. Conventional techniques for predicting protein structure are ineffective, perform poorly, expensive and slow. The reasons for these are due to the vague dissimilar sequences among protein structures, meaningless protein data, high dimensional data, and having to deal with highly imbalanced classification task.  We proposed an Ensemble Neural Network learning model that consists of some Neural Network algorithms such as Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN), Cascade Forward  Network (CFN) and Non-linear Autoregressive Network with Exogenous (NARX) models. These models were trained using training algorithms such as Levenberg-Marquardt (LM), Resilient Back Propagation (RBP) and Scaled Conjugate Gradient (SCG) to improve the performance. Experimental results show that our proposed model has superior performance compared to the other models compared.