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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins
Meenal Chaudhari, Niraj Thapa, Kaushik Roy, et al.
Molecular Omics (2020) Vol. 16, Iss. 5, pp. 448-454
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model
Subash C. Pakhrin, Suresh Pokharel, Pawel Pratyush, et al.
Journal of Proteome Research (2023) Vol. 22, Iss. 8, pp. 2548-2557
Closed Access | Times Cited: 20
Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model
Palistha Shrestha, Jeevan Kandel, Hilal Tayara, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
Non-Histone Protein Methylation: Biological Significance and Bioengineering Potential
Roberto Di Blasi, Oleg Blyuss, John F. Timms, et al.
ACS Chemical Biology (2021) Vol. 16, Iss. 2, pp. 238-250
Open Access | Times Cited: 36
Insights on post-translational modifications in fatty liver and fibrosis progression
C. Nageswara Raju, Kavitha Sankaranarayanan
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (2025) Vol. 1871, Iss. 3, pp. 167659-167659
Closed Access
Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
Md. Easin Arafat, Md. Wakil Ahmad, S.M. Shovan, et al.
Genes (2020) Vol. 11, Iss. 9, pp. 1023-1023
Open Access | Times Cited: 24
RecSNO: Prediction of Protein S-Nitrosylation Sites Using a Recurrent Neural Network
Arslan Siraj, Tuvshinbayar Chantsalnyam, Hilal Tayara, et al.
IEEE Access (2021) Vol. 9, pp. 6674-6682
Open Access | Times Cited: 20
Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
Subash C. Pakhrin, Suresh Pokharel, Hiroto Saigo, et al.
Methods in molecular biology (2022), pp. 285-322
Closed Access | Times Cited: 15
iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features
Thanh‐Hoang Nguyen‐Vo, Quang H. Trinh, Loc Nguyen, et al.
BMC Genomics (2022) Vol. 23, Iss. S5
Open Access | Times Cited: 14
Multifactorial feature extraction and site prognosis model for protein methylation data
Monika Khandelwal, Ranjeet Kumar Rout, Saiyed Umer, et al.
Briefings in Functional Genomics (2022) Vol. 22, Iss. 1, pp. 20-30
Open Access | Times Cited: 13
Prediction of leukemia peptides using convolutional neural network and protein compositions
Seher Ansar Khawaja, Muhammad Shoaib Farooq, Kashif Ishaq, et al.
BMC Cancer (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism
Haiwei Zhou, Wenxi Tan, Shaoping Shi
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 6
DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites
Meenal Chaudhari, Niraj Thapa, Hamid D. Ismail, et al.
Frontiers in Cell and Developmental Biology (2021) Vol. 9
Open Access | Times Cited: 15
CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence
Jiaojiao Zhao, Haoqiang Jiang, Guoyang Zou, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 10
A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
Niraj Thapa, Meenal Chaudhari, Anthony A. Iannetta, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 14
SSMFN: a fused spatial and sequential deep learning model for methylation site prediction
Favorisen Rosyking Lumbanraja, Bharuno Mahesworo, Tjeng Wawan Cenggoro, et al.
PeerJ Computer Science (2021) Vol. 7, pp. e683-e683
Open Access | Times Cited: 11
RMSxAI: arginine methylation sites prediction from protein sequences using machine learning algorithms and explainable artificial intelligence
Gaurav Dwivedi, Monika Khandelwal, Ranjeet Kumar Rout, et al.
Deleted Journal (2024) Vol. 6, Iss. 7
Open Access | Times Cited: 1
Interpretable machine learning identification of arginine methylation sites
Syed Danish Ali, Hilal Tayara, Kil To Chong
Computers in Biology and Medicine (2022) Vol. 147, pp. 105767-105767
Closed Access | Times Cited: 6
LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications
Hongyu Wang, Zhaomin Yao, Renli Luo, et al.
Gene (2023) Vol. 862, pp. 147246-147246
Closed Access | Times Cited: 2
DeepPRMS: advanced deep learning model to predict protein arginine methylation sites
Monika Khandelwal, Ranjeet Kumar Rout
Briefings in Functional Genomics (2024) Vol. 23, Iss. 4, pp. 452-463
Closed Access
Post-Translational Modification Prediction via Prompt-Based Fine-Tuning of a GPT-2 Model
Palistha Shrestha, Jeevan Kandel, Hilal Tayara, et al.
Research Square (Research Square) (2024)
Open Access
UniPTM: Multiple PTM site prediction on full-length protein sequence
Lingkuan Meng, Jiecong Lin, Ke Cheng, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Protein Post-Translational Modifications
Shuchi Sharma
Elsevier eBooks (2024)
Closed Access
PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence
Monika Khandelwal, Ranjeet Kumar Rout
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction
Ling-Yan Gu, Ting-Bo Chen, Jianqiang Li, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 28, Iss. 4, pp. 1917-1926
Closed Access | Times Cited: 1
GRA-GCN: Dense Granule Protein Prediction in Apicomplexa Protozoa Through Graph Convolutional Network
Haoyuan Shi, Haisong Feng, Zhenxiao Lu, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 3, pp. 1963-1970
Closed Access | Times Cited: 2
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