GTDDA: Graph Convolutional Network and Graph Transformer Structure for Drug Repositioning-Reference-Cited by-同舟云学术

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GTDDA: Graph Convolutional Network and Graph Transformer Structure for Drug Repositioning

Published:2023-12-05 Issue: Volume: Page:436-439
ISSN:
Container-title:2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
language:
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Author:

Tang Jialan¹,Zeng Xiaoting¹,Li Pingkang²,Chen Weilin¹,Liu Chuan-Ming³,Lei Baiying⁴

Affiliation:

1. Shenzhen University,College of Computer Science & Software Engineering,Shenzhen,China

2. National Taipei University of Technology,Computer Science and Information Engineering,Taipei,China

3. Shenzhen University Medical School Shenzhen University,Institute of Biological Therapy,Shenzhen,China

4. Shenzhen University Medical School Shenzhen University,School of Biomedical Engineering,Shenzhen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385250/10385251/10386002.pdf?arnumber=10386002

Reference17 articles.

1. Drug repositioning: Progress and challenges in drug discovery for various diseases

2. Predicting drug-disease associations with heterogeneous network embedding

3. Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks

4. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

5. Predicting drug–disease associations through layer attention graph convolutional network

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