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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:

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function
Amelia Villegas-Morcillo, Stavros Makrodimitris, Roeland C. H. J. van Ham, et al.
Bioinformatics (2020) Vol. 37, Iss. 2, pp. 162-170
Open Access | Times Cited: 105

Showing 1-25 of 105 citing articles:

Learning functional properties of proteins with language models
Serbülent Ünsal, Heval Ataş, Muammer Albayrak, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 3, pp. 227-245
Closed Access | Times Cited: 134
Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Rahmad Akbar, Habib Bashour, Puneet Rawat, et al.
mAbs (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 82
Contrastive learning on protein embeddings enlightens midnight zone
Michael Heinzinger, Maria Littmann, Ian Sillitoe, et al.
NAR Genomics and Bioinformatics (2022) Vol. 4, Iss. 2
Open Access | Times Cited: 77
NetGO 3.0: Protein Language Model Improves Large-Scale Functional Annotations
Shaojun Wang, Ronghui You, Yunjia Liu, et al.
Genomics Proteomics & Bioinformatics (2023) Vol. 21, Iss. 2, pp. 349-358
Open Access | Times Cited: 45
Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets
Christian Dallago, Konstantin Schütze, Michael Heinzinger, et al.
Current Protocols (2021) Vol. 1, Iss. 5
Closed Access | Times Cited: 94
Accurate protein function prediction via graph attention networks with predicted structure information
Ben Lai, Jinbo Xu
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 77
Representation learning applications in biological sequence analysis
Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 3198-3208
Open Access | Times Cited: 70
Novel machine learning approaches revolutionize protein knowledge
Nicola Bordin, Christian Dallago, Michael Heinzinger, et al.
Trends in Biochemical Sciences (2022) Vol. 48, Iss. 4, pp. 345-359
Open Access | Times Cited: 48
LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction
Zichen Wang, Steven A. Combs, Ryan Brand, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 47
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
Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction
Yiheng Zhu, Chengxin Zhang, Dong‐Jun Yu, et al.
PLoS Computational Biology (2022) Vol. 18, Iss. 12, pp. e1010793-e1010793
Open Access | Times Cited: 41
Linguistically inspired roadmap for building biologically reliable protein language models
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 5, pp. 485-496
Closed Access | Times Cited: 34
Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions
Doo Nam Kim, Andrew McNaughton, Neeraj Kumar
Bioengineering (2024) Vol. 11, Iss. 2, pp. 185-185
Open Access | Times Cited: 10
Are protein language models the new universal key?
Konstantin Weißenow, Burkhard Rost
Current Opinion in Structural Biology (2025) Vol. 91, pp. 102997-102997
Open Access | Times Cited: 1
Teaching AI to speak protein
Michael Heinzinger, Burkhard Rost
Current Opinion in Structural Biology (2025) Vol. 91, pp. 102986-102986
Open Access | Times Cited: 1
An analysis of protein language model embeddings for fold prediction
Amelia Villegas-Morcillo, Ángel M. Gómez, Victoria Sánchez
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Open Access | Times Cited: 32
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning
Yufan Liu, Boxue Tian
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 21
MM-StackEns: A new deep multimodal stacked generalization approach for protein–protein interaction prediction
Alexandra-Ioana Albu, Maria-Iuliana Bocicor, Gabriela Czibula
Computers in Biology and Medicine (2023) Vol. 153, pp. 106526-106526
Open Access | Times Cited: 19
A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction
Richa Dhanuka, Jyoti Prakash Singh, Anushree Tripathi
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023) Vol. 20, Iss. 3, pp. 2291-2301
Closed Access | Times Cited: 19
CFAGO: cross-fusion of network and attributes based on attention mechanism for protein function prediction
Zhourun Wu, Mingyue Guo, Xiaopeng Jin, et al.
Bioinformatics (2023) Vol. 39, Iss. 3
Open Access | Times Cited: 17
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
Protein Design with Deep Learning
Marianne Defresne, Sophie Barbe, Thomas Schiex
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 21, pp. 11741-11741
Open Access | Times Cited: 39
Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction
Suresh Pokharel, Pawel Pratyush, Hamid D. Ismail, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 21, pp. 16000-16000
Open Access | Times Cited: 13
Clustering FunFams using sequence embeddings improves EC purity
Maria Littmann, Nicola Bordin, Michael Heinzinger, et al.
Bioinformatics (2021) Vol. 37, Iss. 20, pp. 3449-3455
Open Access | Times Cited: 31
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
QHwan Kim, Joon-Hyuk Ko, Sunghoon Kim, et al.
Bioinformatics (2021) Vol. 37, Iss. 20, pp. 3428-3435
Open Access | Times Cited: 29
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