DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins-Reference-Cited by-同舟云学术

DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins

Published:2020 Issue:5 Volume:16 Page:448-454
ISSN:2515-4184
Container-title:Molecular Omics
language:en
Short-container-title:Mol. Omics

Author:

Chaudhari Meenal¹²³⁴,Thapa Niraj¹²³⁴,Roy Kaushik⁵²³⁴,Newman Robert H.⁶²³⁴,Saigo Hiroto⁷⁸⁹¹⁰,B. K. C. Dukka¹¹¹²¹³⁴^ORCID

Affiliation:

1. Department of Computational Science and Engineering

2. North Carolina Agricultural & Technical State University

3. Greensboro

4. USA

5. Department of Computer Science

6. Department of Biology

7. Department of Informatics

8. Kyushu University

9. Fukuoka 819-0395

10. Japan

11. Electrical Engineering and Computer Science Department

12. Wichita State University

13. Wichita

Abstract

DeepRMethylSite is an ensemble-based deep learning model that takes protein sequences as input and predicts sites of Arginine methylation. The implementation and source code are provided at https://github.com/dukkakc/DeepRMethylSite.

Funder

National Institutes of Health

National Science Foundation

Japan Society for the Promotion of Science

Publisher

Royal Society of Chemistry (RSC)

Subject

Genetics,Molecular Biology,Biochemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/MO/D0MO00025F

Reference35 articles.

1. ɛ-N-Methyl-lysine in Bacterial Flagellar Protein

2. The diverse functions of histone lysine methylation

3. Arginine Methylation

4. Regulation of chromatin by histone modifications

5. Identifying and quantifying in vivo methylation sites by heavy methyl SILAC

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DeepPRMS: advanced deep learning model to predict protein arginine methylation sites;Briefings in Functional Genomics;2024-01-24

2. PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence;BMC Bioinformatics;2023-10-04

3. LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model;Journal of Proteome Research;2023-07-17

4. LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications;Gene;2023-04

5. DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism;Briefings in Bioinformatics;2023-01-24