A Review of Deep Learning Techniques for Protein Function Prediction
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Abstract (arXiv)
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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References · 6
- 2017Attention Is All You Needcited by 59,932
- 2018Relational Autoencoder for Feature Extractioncited by 23
- 2017DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifiercited by 20
- 2020DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Predictioncited by 13
- 2017ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Networkcited by 3
- 2017Developing a comprehensive framework for multimodal feature extractioncited by 2
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