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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 20; číslo 5; s. 3182 - 3194
Hlavní autoři:	Luo, Xin, Wang, Liwei, Hu, Pengwei, Hu, Lun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!