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Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation

  • Published:2023-03-01 Issue:2 Volume:20 Page:932-943
  • ISSN:1545-5963
  • Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • language:
  • Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Lin Xuan1,Quan Zhe1ORCID,Wang Zhi-Jie2ORCID,Guo Yan1,Zeng Xiangxiang1ORCID,Yu Philip S.3ORCID

Affiliation:

1. College of Computer Science and Technology, Hunan University, Changsha, China

2. College of Computer Science, Ministry of Education, Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, China

3. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Reference42 articles.

1. Rectified linear units improve restricted Boltzmann machines;nair;Proc 27th Int Conf Mach Learn,2010

2. Chemogenomic approaches to rational drug design

3. How powerful are graph neural networks?;xu;Proc Int Conf Learn Representations,2019

4. A probabilistic approach for collective similarity-based drug–drug interaction prediction

5. Deep Residual Learning for Image Recognition

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