AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree-Reference-Cited by-同舟云学术

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Published:2023-01-17 Issue:3 Volume:63 Page:826-834
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Khan Adnan¹,Uddin Jamal¹,Ali Farman²³^ORCID,Kumar Harish⁴,Alghamdi Wajdi⁵,Ahmad Aftab⁶

Affiliation:

1. Qurtuba University of Science and Information Technology, Peshawar5000, Khyber Pakhtunkhwa, Pakistan

2. Sarhad University of Science and Information Technology, Mardan Campus, Peshawar23200, Pakistan

3. Department of Elementary and Secondary Education Department, Government of Khyber Pakhtunkhwa, Peshawar5000, Khyber Pakhtunkhwa, Pakistan

4. Department of Computer Science, College of Computer Science, King Khalid University, Abha61421, Saudi Arabia

5. Department of Information Technology, Faculty of Computing and Information Technology, King AbdulAziz University, Jeddah21589, Saudi Arabia

6. Department of Computer Science, Abdul Wali Khan University Mardan, Mardan23200, Pakistan

Funder

Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c01417

Reference64 articles.

1. Marine Proteins and Peptides

2. Antifreeze proteins in winter rye

3. Biochemistry of fish antifreeze proteins

4. Biotechnological applications of plant freezing associated proteins

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