A Review of Deep Learning Techniques for Protein Function Prediction

• URL: http://arxiv.org/abs/2211.09705v1
• Date: Thu, 27 Oct 2022 20:30:25 GMT
• Title: A Review of Deep Learning Techniques for Protein Function Prediction
• Authors: Divyanshu Aggarwal and Yasha Hasija
• Abstract summary: This review paper analyzes the recent developments in approaches for the task of predicting protein function using deep learning. We 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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