Diabetes mellitus disease has become a significant human health concern globally, causing cardiovascular complications, kidney disease, cerebrovascular accidents, etc. In humans, bioactive molecules, specifically Antidiabetic peptides (ADPs), target or T-cells to regulate insulin production, making them potential alternatives for diabetes therapy. Recently, several computational ensemble methods have been investigated to predict ADPs. Notwithstanding progress in ensemble techniques, finding optimal solutions for predicting ADPs requires continuous effort for improvement. Therefore, the current study proposed a novel Deep-Q-Network (DQN) -based Reinforcement learning (RL) approach for optimizing a multiview ensemble to predict anti-diabetic peptides and diabetic types. Initially, to obtain the multiview, diverse feature encodings were employed, which were then systematically categorized into three heterogeneous groups based on the information sources and information-extracting techniques. Each group is considered as a view, and these views are based on sequence, physicochemical properties with sequence composition, and evolutionary information. Next, the proposed ensemble is constructed with 36 branch classifiers derived from 6 convolutional neural networks (CNNs). A reinforcement learning technique is employed to optimize the ensemble search space of 2^36-1, made by thirty-six branch CNNs and three views. The DQN approach encodes 36-bit binary strings to find the optimal ensemble. The optimal ensemble with an ideal view-classifier pair enhances antidiabetic prediction efficacy. It surpasses existing state-of-the-art with a classification accuracy of 96. 37%, a matthews correlation coefficient of 0. 870, and an area under the curve of 0. 983 on the standard ADPs dataset. Lastly, the performance of the proposed ensemble is considered to be its efficacy in peptide antidiabetic activity prediction.
Kumar et al. (Thu,) studied this question.