Graph neural networks for protein-protein interactions

Sheng Chang; Yifan Wang; Xinhong Zhang; Fan Zhang

doi:10.1117/12.3006152

10 October 2023 Graph neural networks for protein-protein interactions

Sheng Chang, Yifan Wang, Xinhong Zhang, Fan Zhang

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127994O (2023) https://doi.org/10.1117/12.3006152
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Protein-protein interaction (PPI) is the process by which two or more protein molecules form a protein complex through non-covalent bonds. The interactions between protein and the protein complexes formed by these interactions are the primary complements of various essential cellular functions and carry out almost all vital life activities. Most PPI research today focuses on the use of machine learning methods, of which the use of graph neural networks (GNN) for prediction is a current hot direction. This paper highlights the main advances in the application of GNN to proteinprotein interactions. The first part reviews protein sequence-based methods for PPI prediction. And the second part focuses on structure-based graph neural network PPI prediction methods. Finally, we discuss the shortcomings of GNNs in this area and future directions in PPI prediction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sheng Chang, Yifan Wang, Xinhong Zhang, and Fan Zhang "Graph neural networks for protein-protein interactions", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127994O (10 October 2023); https://doi.org/10.1117/12.3006152

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