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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:

Accuracy and data efficiency in deep learning models of protein expression
Evangelos-Marios Nikolados, Arin Wongprommoon, Oisin Mac Aodha, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 39

Showing 1-25 of 39 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: 66
Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning
Thijs Nieuwkoop, Barbara R. Terlouw, Katherine G. Stevens, et al.
Nucleic Acids Research (2023) Vol. 51, Iss. 5, pp. 2363-2376
Open Access | Times Cited: 24
Deciphering the gut microbiome: The revolution of artificial intelligence in microbiota analysis and intervention
Mohammad Abavisani, Alireza Khoshrou, Sobhan Karbas Foroushan, et al.
Current Research in Biotechnology (2024) Vol. 7, pp. 100211-100211
Open Access | Times Cited: 9
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review
Daniel Schwabe, Katinka Becker, Martin Seyferth, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 8
Large scale foundation models for intelligent manufacturing applications: a survey
Haotian Zhang, Stuart Dereck Semujju, Zhicheng Wang, et al.
Journal of Intelligent Manufacturing (2025)
Open Access
A Paradigm of Computer Vision and Deep Learning Empowers the Strain Screening and Bioprocess Detection
Feng Xu, Su Li, Yuan Wang, et al.
Biotechnology and Bioengineering (2025)
Open Access
Improving the generalization of protein expression models with mechanistic sequence information
Yuxin Shen, Grzegorz Kudla, Diego A. Oyarzún
Nucleic Acids Research (2025) Vol. 53, Iss. 3
Open Access
How many specimens make a sufficient training set for automated three-dimensional feature extraction?
James M. Mulqueeney, Alex Searle‐Barnes, Anieke Brombacher, et al.
Royal Society Open Science (2024) Vol. 11, Iss. 6
Open Access | Times Cited: 4
Current limitations in predicting mRNA translation with deep learning models
Niels Schlusser, Asier González, Muskan Pandey, et al.
Genome biology (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 4
Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors
Gloria J. Zhou, Fuzhong Zhang
Biosensors (2023) Vol. 13, Iss. 4, pp. 428-428
Open Access | Times Cited: 13
Effective drug-target affinity prediction via generative active learning
Yuansheng Liu, Zhenran Zhou, Xiaofeng Cao, et al.
Information Sciences (2024) Vol. 679, pp. 121135-121135
Closed Access | Times Cited: 4
How many specimens make a sufficient training set for automated 3D feature extraction?
James M. Mulqueeney, Alex Searle‐Barnes, Anieke Brombacher, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
Deep learning for optimization of protein expression
Evangelos-Marios Nikolados, Diego A. Oyarzún
Current Opinion in Biotechnology (2023) Vol. 81, pp. 102941-102941
Open Access | Times Cited: 11
Current limitations in predicting mRNA translation with deep learning models
Niels Schlusser, Asier González, Muskan Pandey, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
Bayesian Optimization for Design of Multiscale Biological Circuits
Charlotte Merzbacher, Oisin Mac Aodha, Diego A. Oyarzún
ACS Synthetic Biology (2023) Vol. 12, Iss. 7, pp. 2073-2082
Open Access | Times Cited: 9
Applications of artificial intelligence and machine learning in dynamic pathway engineering
Charlotte Merzbacher, Diego A. Oyarzún
Biochemical Society Transactions (2023) Vol. 51, Iss. 5, pp. 1871-1879
Open Access | Times Cited: 9
Challenges and opportunities in applying AI to evolutionary morphology
Yichen He, James M. Mulqueeney, Emily Watt, et al.
(2024)
Open Access | Times Cited: 2
Transfer learning for cross-context prediction of protein expression from 5’UTR sequence
Pierre-Aurélien Gilliot, Thomas E. Gorochowski
Nucleic Acids Research (2024) Vol. 52, Iss. 13, pp. e58-e58
Open Access | Times Cited: 2
Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape
Andreas Wagner
Bioinformatics (2024) Vol. 40, Iss. 5
Open Access | Times Cited: 1
Machine learning-assisted substrate binding pocket engineering based on structural information
Xinglong Wang, Kangjie Xu, Xuan Zeng, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 1
Machine learning-based early prediction of rice-growing fields using multi-temporal Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral data
Nguyễn Thanh Sơn, Chi-Farn Chen, Huan-Sheng Lin, et al.
Journal of Applied Remote Sensing (2024) Vol. 18, Iss. 03
Closed Access | Times Cited: 1
The application of machine learning in 3D/4D printed stimuli-responsive hydrogels
Onome Ejeromedoghene, Moses Kumi, Ephraim Akor, et al.
Advances in Colloid and Interface Science (2024) Vol. 336, pp. 103360-103360
Closed Access | Times Cited: 1
Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes
Heidi E. Klumpe, Jean‐Baptiste Lugagne, Ahmad S. Khalil, et al.
ACS Synthetic Biology (2023) Vol. 12, Iss. 8, pp. 2367-2381
Open Access | Times Cited: 2
DNA representations and generalization performance of sequence-to-expression models
Yuxin Shen, Grzegorz Kudla, Diego A. Oyarzún
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
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