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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:

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2019)
Open Access | Times Cited: 233

Showing 1-25 of 233 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
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Vol. 44, Iss. 10, pp. 7112-7127
Open Access | Times Cited: 958
Machine-learning-guided directed evolution for protein engineering
Kevin Yang, Zachary Wu, Frances H. Arnold
Nature Methods (2019) Vol. 16, Iss. 8, pp. 687-694
Open Access | Times Cited: 823
Modeling aspects of the language of life through transfer-learning protein sequences
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, et al.
BMC Bioinformatics (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 481
Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2019)
Open Access | Times Cited: 450
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
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
Expanding functional protein sequence spaces using generative adversarial networks
Donatas Repecka, Vykintas Jauniškis, Laurynas Karpus, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 4, pp. 324-333
Open Access | Times Cited: 261
High-resolutionde novostructure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 258
Autonomous Discovery in the Chemical Sciences Part II: Outlook
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Angewandte Chemie International Edition (2019) Vol. 59, Iss. 52, pp. 23414-23436
Open Access | Times Cited: 232
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: 229
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
MSA Transformer
Roshan Rao, Jason Liu, Robert Verkuil, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 215
Machine learning in protein structure prediction
Mohammed AlQuraishi
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 1-8
Open Access | Times Cited: 206
DeepGOPlus: improved protein function prediction from sequence
Maxat Kulmanov, Robert Hoehndorf
Bioinformatics (2021) Vol. 37, Iss. 8, pp. 1187-1187
Open Access | Times Cited: 191
Fast and Flexible Protein Design Using Deep Graph Neural Networks
Alexey Strokach, David Becerra, Carles Corbi‐Verge, et al.
Cell Systems (2020) Vol. 11, Iss. 4, pp. 402-411.e4
Open Access | Times Cited: 179
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
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
Blockwise Self-Attention for Long Document Understanding
Jiezhong Qiu, Hao Ma, Omer Levy, et al.
(2020)
Open Access | Times Cited: 137
Embeddings from deep learning transfer GO annotations beyond homology
Maria Littmann, Michael Heinzinger, Christian Dallago, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 133
Protein sequence design with deep generative models
Zachary Wu, Kadina E. Johnston, Frances H. Arnold, et al.
Current Opinion in Chemical Biology (2021) Vol. 65, pp. 18-27
Open Access | Times Cited: 127
Advances in machine learning for directed evolution
Bruce J. Wittmann, Kadina E. Johnston, Zachary Wu, et al.
Current Opinion in Structural Biology (2021) Vol. 69, pp. 11-18
Open Access | Times Cited: 125
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