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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:

Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 309

Showing 1-25 of 309 citing articles:

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 15
Open Access | Times Cited: 1827
A guide to machine learning for biologists
Joe G. Greener, Shaun M. Kandathil, Lewis Moffat, et al.
Nature Reviews Molecular Cell Biology (2021) Vol. 23, Iss. 1, pp. 40-55
Open Access | Times Cited: 1156
Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R. Greene, et al.
Nature Biotechnology (2023) Vol. 41, Iss. 8, pp. 1099-1106
Open Access | Times Cited: 514
ProteinBERT: a universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg, et al.
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2102-2110
Open Access | Times Cited: 445
ProtGPT2 is a deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, Birte Höcker
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 369
Learning the protein language: Evolution, structure, and function
Tristan Bepler, Bonnie Berger
Cell Systems (2021) Vol. 12, Iss. 6, pp. 654-669.e3
Open Access | Times Cited: 330
Deep Learning applications for COVID-19
Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 298
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Payel Das, Tom Sercu, Kahini Wadhawan, et al.
Nature Biomedical Engineering (2021) Vol. 5, Iss. 6, pp. 613-623
Open Access | Times Cited: 283
Protein design and variant prediction using autoregressive generative models
Jung-Eun Shin, Adam J. Riesselman, Aaron W. Kollasch, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 269
The language of proteins: NLP, machine learning & protein sequences
Dan Ofer, Nadav Brandes, Michal Linial
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 1750-1758
Open Access | Times Cited: 262
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Jesse Vig, Ali Madani, Lav R. Varshney, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 227
flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions
Gang Hu, Akila Katuwawala, Kui Wang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 221
Transformer protein language models are unsupervised structure learners
Roshan Rao, Joshua Meier, Tom Sercu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 219
PredictProtein - Predicting Protein Structure and Function for 29 Years
Michael Bernhofer, Christian Dallago, Tim Karl, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. W1, pp. W535-W540
Open Access | Times Cited: 203
ProGen: Language Modeling for Protein Generation
Ali Madani, Bryan McCann, Nikhil Naik, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 186
Genome-wide prediction of disease variant effects with a deep protein language model
Nadav Brandes, Grant Goldman, Charlotte H. Wang, et al.
Nature Genetics (2023) Vol. 55, Iss. 9, pp. 1512-1522
Open Access | Times Cited: 180
Deep Learning in Protein Structural Modeling and Design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, et al.
Patterns (2020) Vol. 1, Iss. 9, pp. 100142-100142
Open Access | Times Cited: 179
Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R. Jamasb, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 174
Informed training set design enables efficient machine learning-assisted directed protein evolution
Bruce J. Wittmann, Yisong Yue, Frances H. Arnold
Cell Systems (2021) Vol. 12, Iss. 11, pp. 1026-1045.e7
Open Access | Times Cited: 152
UDSMProt: universal deep sequence models for protein classification
Nils Strodthoff, Patrick Wagner, Markus Wenzel, et al.
Bioinformatics (2020) Vol. 36, Iss. 8, pp. 2401-2409
Open Access | Times Cited: 150
Generating functional protein variants with variational autoencoders
Alex Hawkins‐Hooker, Florence Depardieu, Sebastien Baur, et al.
PLoS Computational Biology (2021) Vol. 17, Iss. 2, pp. e1008736-e1008736
Open Access | Times Cited: 149
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Yunan Luo, Guangde Jiang, Tianhao Yu, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 122
Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation
Harini Narayanan, Fabian Dingfelder, Alessandro Butté, et al.
Trends in Pharmacological Sciences (2021) Vol. 42, Iss. 3, pp. 151-165
Closed Access | Times Cited: 118
Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 108
Learning meaningful representations of protein sequences
Nicki Skafte Detlefsen, Søren Hauberg, Wouter Boomsma
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 106
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