Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units-Reference-Cited by-同舟云学术

Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units

Published:2022-10-27 Issue:44 Volume:7 Page:40569-40577
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Zhao Zhengyun¹,Gui Jingyu¹,Yao Anqi¹,Le Nguyen Quoc Khanh²³⁴^ORCID,Chua Matthew Chin Heng¹^ORCID

Affiliation:

1. Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore 119615, Singapore

2. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan

3. Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan

4. Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan

Funder

Ministry of Science and Technology, Taiwan

Ministry of Education

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c05881

Reference30 articles.

1. Peptide-based therapeutic vaccines for allergic and autoimmune diseases

2. Peptide therapeutics: current status and future directions

3. ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning

4. ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity

5. BLAST: improvements for better sequence analysis

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