GraMDTA: Multimodal Graph Neural Networks for Predicting Drug-Target Associations-Reference-Cited by-同舟云学术

GraMDTA: Multimodal Graph Neural Networks for Predicting Drug-Target Associations

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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Author:

Yella Jaswanth K.¹,Ghandikota Sudhir K.¹,Jegga Anil G.²

Affiliation:

1. University of Cincinnati,Department of Electrical Engineering and Computer Science,OH,USA

2. Cincinnati Children’s Hospital Medical Center,Division of Biomedical Informatics,OH,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995245.pdf?arnumber=9995245

Reference51 articles.

1. NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions

2. Drug-Disease Association Prediction Based on Neighborhood Information Aggregation in Neural Networks

3. Inductive Representation Learning on Large Graphs;hamilton;Advances in neural information processing systems,2017

4. Drug similarity integration through attentive multi-view graph autoencoders;ma;IJCAI International Joint Conference on Artificial Intelligence,2018

5. MGATRx: Discovering Drug Repositioning Candidates Using Multi-view Graph Attention;yella;IEEE/ACM Transactions on Computational Biology and Bioinformatics,2021

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