Application of Deep learning Algorithms On Protein Function Prediction: A Systematic Review

Abstract

Protein function encompasses a wide variety of protein activities. Gene regulation, material movement, and biological events that are catalyzed by enzymes are a few examples of this. They perform their functions by interacting with other proteins in a specific biological process. Deep learning techniques have an advantage over traditional machine learning techniques in protein function prediction in that they can directly extract features from data and identify nonlinear connections between abstract characteristics. Consequently, Deep learning techniques have so gained extraordinary popularity in recent years and have been successfully used to solve a variety of problems. The main goal of this paper is to provide a systematic review on studies done on the application of deep learning algorithms on protein function prediction studies. The results indicated that using deep learning techniques to predict protein function is the primary development path at the moment. For current applications of protein function prediction, a single deep learning algorithm is no longer adequate, and numerous multi-algorithm combinations of prediction approaches have therefore evolved.