Graph-based prediction of Protein-protein interactions with attributed signed graph embedding

Background Protein-protein interactions (PPIs) are central to many biological processes. Considering that the experimental methods for identifying PPIs are time-consuming and expensive, it is important to develop automated computational methods to better predict PPIs. Various machine learning method...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 21; no. 1; pp. 1 - 16
Main Authors:	Yang, Fang, Fan, Kunjie, Song, Dandan, Lin, Huakang
Format:	Journal Article
Language:	English
Published:	London BioMed Central 21.07.2020 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Accuracy Algorithms Bioinformatics Biological activity Biomedical and Life Sciences Coders Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Datasets Deep learning Drosophila E coli Embedding Escherichia coli Experimental methods Graph neural networks Graphical representations Identification methods Learning algorithms Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Methodology Methodology Article Microarrays Network embedding Neural networks Predictions Protein interaction Protein-protein interaction Protein-protein interactions Proteins Representation learning Signal transduction Social networks Statistical methods Variational graph auto-encoder Representation learning Network embedding Protein-protein interaction Variational graph auto-encoder
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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