Integrating Multi-view Feature Extraction and Fuzzy Rank-Based Ensemble for Accurate HIV-1 Protease Cleavage Site Prediction

Acquired immunodeficiency syndrome (AIDS) continues to be a significant cause of mortality, disability, and economic repercussions, especially in underdeveloped countries. Extensive research has been conducted to develop effective therapies for human immunodeficiency virus (HIV) infection, including the prediction of HIV-1 protease cleavage sites. Accurate prediction of these sites can expedite the discovery of new HIV-1 protease inhibitors. Motivated by this, we propose a novel approach for HIV-1 protease cleavage site prediction using numerical descriptors based on octapeptide sequences. Our method incorporates multi-view feature extraction, combining sequence order effects of amino acids with physicochemical features. To capture important information, we utilize a convolutional neural network for feature extraction. For the classification task, we employ a fuzzy rank-based ensemble method, utilizing Random Forest, Logistic Regression, and Support Vector Machine as base classifiers. The ensemble combines their predictions to make the final prediction. Experimental evaluation on benchmark datasets demonstrates the effectiveness of our approach, achieving average Accuracy, AUC, Precision, Recall, and F-measure of 0.93, 0.95, 0.85, 0.76, and 0.80, respectively. Comparisons with existing studies confirm the potential of our proposed technique. The source code can be downloaded from Github: https://github.com/SusmitaPalmal/RC_CNN_HIV .