Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models-Reference-Cited by-同舟云学术

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Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models

Published:2024-09 Issue:9 Volume:28 Page:5649-5657
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Ran Zixu¹^ORCID,Wang Cong¹,Sun Heyun²,Pan Shirui³^ORCID,Li Fuyi¹^ORCID

Affiliation:

1. College of Information Engineering, Northwest A&F University, Xianyang, China

2. South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, SA, Australia

3. School of Information and Communication Technology, Griffith University, Brisbane, QLD, Australia

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Chuangyuan Innovation and Entrepreneurship Talent Project

Northwest A&F University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6221020/10666866/10555145.pdf?arnumber=10555145

Reference53 articles.

1. Identification of a Bacterial Type III Effector Family with G Protein Mimicry Functions

2. Protein delivery into eukaryotic cells by type III secretion machines

3. Bacterial Injectisomes: Needle Length Does Matter

4. Bastion3: a two-layer ensemble predictor of type III secreted effectors

5. Structural mimicry in bacterial virulence

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