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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:

Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, et al.
Nature Methods (2019) Vol. 16, Iss. 12, pp. 1315-1322
Open Access | Times Cited: 884

Showing 1-25 of 884 citing articles:

Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, et al.
Nature (2021) Vol. 596, Iss. 7873, pp. 583-589
Open Access | Times Cited: 28635
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
Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, et al.
Science (2023) Vol. 379, Iss. 6637, pp. 1123-1130
Open Access | Times Cited: 1818
SignalP 6.0 predicts all five types of signal peptides using protein language models
Felix Teufel, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 7, pp. 1023-1025
Open Access | Times Cited: 1546
AI in health and medicine
Pranav Rajpurkar, Emma Chen, Oishi Banerjee, et al.
Nature Medicine (2022) Vol. 28, Iss. 1, pp. 31-38
Closed Access | Times Cited: 1274
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: 1132
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 1104
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: 950
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: 507
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
Directed Evolution: Methodologies and Applications
Yajie Wang, Pu Xue, Mingfeng Cao, et al.
Chemical Reviews (2021) Vol. 121, Iss. 20, pp. 12384-12444
Closed Access | Times Cited: 394
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
Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 309
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Lifan Chen, Xiaoqin Tan, Dingyan Wang, et al.
Bioinformatics (2020) Vol. 36, Iss. 16, pp. 4406-4414
Open Access | Times Cited: 304
Low-N protein engineering with data-efficient deep learning
Surojit Biswas, Grigory Khimulya, Ethan C. Alley, et al.
Nature Methods (2021) Vol. 18, Iss. 4, pp. 389-396
Open Access | Times Cited: 295
Single-sequence protein structure prediction using a language model and deep learning
Ratul Chowdhury, Nazim Bouatta, Surojit Biswas, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 11, pp. 1617-1623
Open Access | Times Cited: 279
AlphaFold at CASP13
Mohammed AlQuraishi
Bioinformatics (2019) Vol. 35, Iss. 22, pp. 4862-4865
Open Access | Times Cited: 273
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
Recent trends in biocatalysis
Dong Yi, Thomas Bayer, Christoffel P. S. Badenhorst, et al.
Chemical Society Reviews (2021) Vol. 50, Iss. 14, pp. 8003-8049
Open Access | Times Cited: 264
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
Learning the language of viral evolution and escape
Brian Hie, Ellen D. Zhong, Bonnie Berger, et al.
Science (2021) Vol. 371, Iss. 6526, pp. 284-288
Open Access | Times Cited: 260
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
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
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