Graph-RPI: predicting RNA–protein interactions via graph autoencoder and self-supervised learning strategies

Abstract RNA–protein interactions (RPIs) are essential for many biological functions and are associated with various diseases. Traditional methods for detecting RPIs are labor-intensive and costly, necessitating efficient computational methods. In this study, we proposed a novel sequence-based RPI p...

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Veröffentlicht in:	Briefings in bioinformatics Jg. 26; H. 3
Hauptverfasser:	Guan, Jiahui, Yao, Lantian, Xie, Peilin, Zhao, Zhihao, Meng, Dian, Lee, Tzong-Yi, Wang, Junwen, Chiang, Ying-Chih
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 01.05.2025 Oxford Publishing Limited (England)
Schlagworte:	Accuracy Algorithms Autoencoder Computational Biology - methods Graph neural networks Graphical representations Humans Machine learning Neural networks Neural Networks, Computer Predictions Problem Solving Protocol Protein interaction Proteins RNA - chemistry RNA - metabolism RNA-Binding Proteins - metabolism Self-supervised learning Supervised Machine Learning graph neural networks self-supervised learning multi-feature fusion RNA–protein interactions
ISSN:	1467-5463, 1477-4054, 1477-4054
Online-Zugang:	Volltext
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