A Review of Deep Learning Techniques for Protein Function Prediction

TL;DR

This review paper discusses recent deep learning methods for protein function prediction, highlighting advancements, state-of-the-art models, and their impact on bioinformatics and computational biology.

Contribution

It provides a comprehensive overview of deep learning techniques applied to protein function prediction, emphasizing recent breakthroughs and future directions.

Findings

Deep learning models have achieved groundbreaking results in protein function prediction.

Modern SOTA models from vision and NLP are now applied to bioinformatics.

The review encourages further research in deep learning for biological sciences.

Abstract

Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification. This review paper analyzes the recent developments in approaches for the task of predicting protein function using deep learning. We explain the importance of determining the protein function and why automating the following task is crucial. Then, after reviewing the widely used deep learning techniques for this task, we continue our review and highlight the emergence of the modern State of The Art (SOTA) deep learning models which have achieved groundbreaking results in the field of computer vision, natural language processing and multi-modal learning in the last few years. We hope that this review will provide a broad view of the current role and advances of deep learning in biological sciences, especially in predicting protein function tasks and encourage new researchers to contribute to this area.