Deep ensemble model for sequence-based prediction of PPI: Self improved optimization assisted intelligent model

PPIs play a significant function in many biological processes. In many different areas, DL algorithms have delivered excellent results, but PPI prediction is one where they fall short. To offer a sequence-based prediction of PPI, this work employs a deep ensemble model. In the beginning, traits including "enhanced semantic similarity, features based on gene ontologies, and sequence-based features" are extracted. A deep ensemble model is introduced that combines models like Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Deep Max out (DMO), and Deep Belief Network (DBN)" is then used to predict the outcomes of the retrieved features. To improve the prediction model, the training is done by the Chaotic Initialized COOT Optimization Algorithm (CI-COA) by optimizing the training weights of DCNN. The performance of the chosen strategy is finally shown through several measures.