An Effective Model for Predicting Phage-Host Interactions Via Graph Embedding Representation Learning With Multi-Head Attention Mechanism-Reference-Cited by-同舟云学术

An Effective Model for Predicting Phage-Host Interactions Via Graph Embedding Representation Learning With Multi-Head Attention Mechanism

Published:2023-06 Issue:6 Volume:27 Page:3061-3071
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Wang Yue¹^ORCID,Sun Han²^ORCID,Wang Haodong¹^ORCID,Li Dandan¹^ORCID,Zhao Weizhong¹^ORCID,Jiang Xingpeng¹,Shen Xianjun¹^ORCID

Affiliation:

1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer, and National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, China

2. School of Mathematics and Statistics, Central China Normal University, Wuhan, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province of China

Fundamental Research Funds for Central Universities

Scientific Research Center Program of National Language Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10144459/10081356.pdf?arnumber=10081356

Reference39 articles.

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2. Adam: A Method for Stochastic Optimization;kingma;Proc Int Conf Learn Representations,0

3. Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

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

5. A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

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