Robust Deep Neural Network-Based Framework for Predicting and Classifying Capsid Protein Based on Biomedical Data-Reference-Cited by-同舟云学术

Robust Deep Neural Network-Based Framework for Predicting and Classifying Capsid Protein Based on Biomedical Data

Published:2023 Issue: Volume:11 Page:107412-107428
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Khattak Anees Ur Rahman¹^ORCID,Ullah Amin¹^ORCID,Rehman Amjad²,Mahmood Tariq²^ORCID,Khattak Qamar Wahid³^ORCID,Alotaibi Sarah⁴^ORCID,Bahaj Saeed Ali Omer⁵^ORCID

Affiliation:

1. Department of Computer Science, Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Pakistan

2. Artificial Intelligence and Data Analytics (AIDA) Laboratory, CCIS, Prince Sultan University, Riyadh, Saudi Arabia

3. Department of Physical Therapy, NCS University System, Peshawar, Pakistan

4. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

5. MIS Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10264080.pdf?arnumber=10264080

Reference59 articles.

1. Myristylation of picornavirus capsid protein VP4 and its structural significance

2. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

3. Topologically Linked Protein Rings in the Bacteriophage HK97 Capsid

4. iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

5. Identification of Capsid/Coat Related Protein Folds and Their Utility for Virus Classification

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