Artificial intelligence for template-free protein structure prediction: a comprehensive review-Reference-Cited by-同舟云学术

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Artificial intelligence for template-free protein structure prediction: a comprehensive review

Published:2022-12-17 Issue: Volume: Page:
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Mufassirin M. M. Mohamed^ORCID,Newton M. A. Hakim,Sattar Abdul

Funder

Australian Research Council

AHEAD Operation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-022-10350-x.pdf

Reference321 articles.

1. Adhikari B (2020) DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout. Bioinformatics 36(2):470–477

2. Adhikari B (2020) A fully open-source framework for deep learning protein real-valued distances. Sci Rep 10(1):1–10

3. Adhikari B, Cheng J (2016) Protein residue contacts and prediction methods. In: Data Mining Techniques for the Life Sciences, pp. 463–476. Springer, Switzerland

4. Adhikari B, Cheng J (2018) CONFOLD2: improved contact-driven ab initio protein structure modeling. BMC Bioinf 19(1):1–5

5. Adhikari B, Bhattacharya D, Cao R, Cheng J (2015) CONFOLD: residue-residue contact-guided ab initio protein folding. Proteins 83(8):1436–1449

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