DTL-NeddSite: A Deep-Transfer Learning Architecture for Prediction of Lysine Neddylation Sites-Reference-Cited by-同舟云学术

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DTL-NeddSite: A Deep-Transfer Learning Architecture for Prediction of Lysine Neddylation Sites

Published:2023 Issue: Volume:11 Page:51798-51809
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Xu Deli¹^ORCID,Zhu Yafei¹,Xu Qiang²,Liu Yuhai³,Chen Yu¹,Zou Yang⁴,Li Lei¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Qingdao University, Qingdao, China

2. Department of Geriatrics, The Affiliated Hospital of Qingdao University, Qingdao, China

3. Dawning International Information Industry Company Ltd., Qingdao, China

4. Faculty of Biomedical and Rehabilitation Engineering, University of Health and Rehabilitation Sciences, Qingdao, China

Funder

National Natural Science Foundation of China

Innovation Capability Improvement Project of Science and Technology for Small- and Medium-Sized Enterprises in Shandong Province

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/ielx7/6287639/10005208/10134527.pdf?arnumber=10134527

Reference48 articles.

1. Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components

2. Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models

3. Prediction of neddylation sites from protein sequences and sequence-derived properties

4. A Comprehensive Survey on Transfer Learning

5. A deep learning framework for modeling structural features of RNA-binding protein targets

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1. DeepNphos: A deep-learning architecture for prediction of N-phosphorylation sites;Computers in Biology and Medicine;2024-03

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