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Deep Learning Methods for Protein Function Prediction
  • Frimpong Boadu,
  • Ahhyun Lee,
  • Jianlin Cheng
Frimpong Boadu
University of Missouri
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Ahhyun Lee
University of Missouri
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Jianlin Cheng
University of Missouri

Corresponding Author:[email protected]

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Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.
27 Jan 2024Submitted to PROTEOMICS
01 Feb 2024Assigned to Editor
01 Feb 2024Submission Checks Completed
01 Feb 2024Review(s) Completed, Editorial Evaluation Pending
01 Feb 2024Reviewer(s) Assigned