DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network

DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network

Motivation: In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the...

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Bibliographic Details
Main Authors: Saranya Muniyappan, Arockia Xavier Annie Rayan, Geetha Thekkumpurath Varrieth
Format: Article
Language:English
Published: AIMS Press 2023-03-01
Series:Mathematical Biosciences and Engineering
Subjects:
drug-target interaction prediction
similarity network integration
information entropy-based random walk
multi-view convolutional neural network
meta-graph-based representation learning
graph neural network
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2023419?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2023419?viewType=HTML

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