Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Improving protein succinylation sites prediction using embeddings from protein language model
Suresh Pokharel, Pawel Pratyush, Michael Heinzinger, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 45

Showing 1-25 of 45 citing articles:

Machine Learning-Guided Protein Engineering
Petr Kouba, Pavel Kohout, Faraneh Haddadi, et al.
ACS Catalysis (2023) Vol. 13, Iss. 21, pp. 13863-13895
Open Access | Times Cited: 84
Machine learning approaches in predicting allosteric sites
Francho Nerín-Fonz, Zoe Cournia
Current Opinion in Structural Biology (2024) Vol. 85, pp. 102774-102774
Open Access | Times Cited: 16
LMNglyPred: prediction of human N-linked glycosylation sites using embeddings from a pre-trained protein language model
Subash C. Pakhrin, Suresh Pokharel, Kiyoko F. Aoki‐Kinoshita, et al.
Glycobiology (2023) Vol. 33, Iss. 5, pp. 411-422
Closed Access | Times Cited: 23
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
pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model
Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 16
M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings
Aditi Shenoy, Yogesh Kalakoti, Durai Sundar, et al.
Bioinformatics (2024) Vol. 40, Iss. 1
Open Access | Times Cited: 6
LMCrot: an enhanced protein crotonylation site predictor by leveraging an interpretable window-level embedding from a transformer-based protein language model
Pawel Pratyush, Soufia Bahmani, Suresh Pokharel, et al.
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 6
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
EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction
Xiaoyang Hou, Yu Wang, Dongbo Bu, et al.
Bioinformatics (2023) Vol. 39, Iss. 11
Open Access | Times Cited: 14
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation
Zhengyi Li, Menglu Li, Lida Zhu, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 1, pp. 188-196
Open Access | Times Cited: 4
Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique
Thi-Xuan Tran, Nguyen Quoc Khanh Le, Van-Nui Nguyen
Computers in Biology and Medicine (2025) Vol. 186, pp. 109664-109664
Closed Access
Challenges and compromises: Predicting unbound antibody structures with deep learning
Alexander Greenshields‐Watson, Odysseas Vavourakis, Fabian C. Spoendlin, et al.
Current Opinion in Structural Biology (2025) Vol. 90, pp. 102983-102983
Open Access
pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network
Shahid Akbar, Ali Raza, Hamid Hussain Awan, et al.
ACS Omega (2025)
Open Access
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 12
ProteinCLIP: enhancing protein language models with natural language
Kevin Wu, Howard F. Chang, James Zou
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 4
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary Structure Prediction
Wei Yang, Chun Liu, Li Zheng
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 7
Machine learning approaches in predicting allosteric sites
Francho Nerín-Fonz, Zoe Cournia
(2024)
Open Access | Times Cited: 2
Transformer Encoder with Protein Language Model for Protein Secondary Structure Prediction
Ammar Kazm, A Orouji Ali, Haslina Hashim
Engineering Technology & Applied Science Research (2024) Vol. 14, Iss. 2, pp. 13124-13132
Open Access | Times Cited: 2
Pathogen dynamics and discovery of novel viruses and enzymes by deep nucleic acid sequencing of wastewater
Emanuel Wyler, Chris Lauber, Artür Manukyan, et al.
Environment International (2024) Vol. 190, pp. 108875-108875
Open Access | Times Cited: 2
Accurate prediction of antibody deamidations by combining high-throughput automated peptide mapping and protein language model-based deep learning
Ben Niu, Benjamin C. Lee, Lili Wang, et al.
(2024)
Open Access | Times Cited: 1
Miniaturizing, Modifying, and Augmenting Nature’s Proteins with Raygun
Kapil Devkota, Daichi Shonai, J. G. Mao, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
TransMHCII: a novel MHC-II binding prediction model built using a protein language model and an image classifier
Xin Yu, Christopher Negron, Lili Huang, et al.
Antibody Therapeutics (2023) Vol. 6, Iss. 2, pp. 137-146
Open Access | Times Cited: 4
Protein embeddings improve phage-host interaction prediction
Mark Edward M. Gonzales, Jennifer C. Ureta, Anish Man Singh Shrestha
PLoS ONE (2023) Vol. 18, Iss. 7, pp. e0289030-e0289030
Open Access | Times Cited: 4
Hybrid protein-ligand binding residue prediction with protein language models: Does the structure matter?
Hamza Gamouh, Marián Novotný, David Hoksza
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 4
ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs
Xiaohu Hao, Long Fan
European Journal of Pharmaceutical Sciences (2024) Vol. 194, pp. 106705-106705
Open Access | Times Cited: 1
Page 1 - Next Page

Scroll to top