AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides-Reference-Cited by-同舟云学术

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AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides

Published:2024-02-26 Issue:5 Volume:64 Page:1751-1762
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Yang Zuojun¹²,Wu Yuhan¹²,Liu Hao¹²,He Li¹²,Deng Xiaoyuan¹²^ORCID

Affiliation:

1. MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China

2. Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China

Funder

Guangzhou Municipal Science and Technology Project

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c02035

Reference56 articles.

1. Role of the cell membrane interface in modulating production and uptake of Alzheimer's beta amyloid protein

2. Biology of Amyloid: Structure, Function, and Regulation

3. Structural Studies of Amyloid Proteins at the Molecular Level

4. A new era for understanding amyloid structures and disease

5. Amyloid Structure: Conformational Diversity and Consequences

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Proteomic evidence for amyloidogenic cross-seeding in fibrinaloid microclots;2024-07-17

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