A computational predictor for autophagy-related proteins using interpretable machine learning and genetic algorithm

Abstract

Autophagy is a quintessential process for eliminating molecules, subcellular elements, and damaged organelles to enhance homeostasis, differentiation, development, and survival. Therefore, a thorough understanding of the sophisticated mechanism of autophagy can solely contribute to the knowledge of side effects, drug repurposing, and the development of novel poly-pharmacological strategies regarding autophagy-related diseases. Artificial intelligence approaches' broad applicability in system biology has been a promising method in identifying the autophagy-related (Art) protein, which is vitally important to regulate and control various stages of autophagy formation. Underlying explainable XGBoost and SHapley Additive exPlanations (SHAP) models, the important features and predictive model of Art protein were established. Consequently, our model performance achieved a sensitivity of 66.52 %, specificity of 82.77 %, accuracy of 77.32 %, and MCC of 0.430 via a 5-fold cross-validation evaluation. Moreover, we evaluated our model on an independent dataset, and the final results reached a sensitivity of 65.1 %, specificity of 79.1 %, and accuracy of 77.1 %. It is then observed that our model was efficient and strongly recommended for further analysis of physiological mechanisms and the development of drugs regarding autophagy. The model and dataset are freely accessible via https://github.com/khanhlee/art-predictor.