Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other word...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 24; no. 1; pp. 151 - 21
Main Authors:	Chen, Peng, Zheng, Haoran
Format:	Journal Article
Language:	English
Published:	London BioMed Central 17.04.2023 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Analysis Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Datasets Deep learning Drug Development - methods Drug discovery Drug Discovery - methods Drug interactions Drug-target interaction Drugs Embedding Graph convolutional autoencoder Graphical representations Invariance Life Sciences Ligands Machine learning Medical care, Cost of Methods Microarrays Neural networks Nodes Polypharmacy Predictions Proteins Spatial consistency constraint China Deep learning Spatial consistency constraint Graph convolutional autoencoder Drug-target interaction
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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