Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this p...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics Jg. 20; H. 5; S. 3182 - 3194
Hauptverfasser:	Luo, Xin, Wang, Liwei, Hu, Pengwei, Hu, Lun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Amino acid sequence Artificial neural networks Biological system modeling Computational modeling Feature extraction Graph neural networks Neural networks PPI network PPI prediction Prediction algorithms Predictions Predictive models Principal components analysis Protein interaction Protein sequences Protein-protein Interaction Proteins Proteomics Task analysis Variational graph autoencoder
ISSN:	1545-5963, 1557-9964, 1557-9964
Online-Zugang:	Volltext
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