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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:

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

Showing 1-25 of 150 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
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
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
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
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
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: 130
Anticancer peptides prediction with deep representation learning features
Zhibin Lv, Feifei Cui, Quan Zou, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 107
Deep learning for drug repurposing: Methods, databases, and applications
Xiaoqin Pan, Xuan Lin, Dongsheng Cao, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 4
Open Access | Times Cited: 94
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: 43
Deep Learning in Proteomics
Bo Wen, Wen‐Feng Zeng, Yuxing Liao, et al.
PROTEOMICS (2020) Vol. 20, Iss. 21-22
Open Access | Times Cited: 126
DeepTFactor: A deep learning-based tool for the prediction of transcription factors
Gi Bae Kim, Ye Gao, Bernhard Ø. Palsson, et al.
Proceedings of the National Academy of Sciences (2020) Vol. 118, Iss. 2
Open Access | Times Cited: 89
Incorporating Machine Learning into Established Bioinformatics Frameworks
Noam Auslander, Ayal B. Gussow, Eugene V. Koonin
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 6, pp. 2903-2903
Open Access | Times Cited: 83
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: 65
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
Sajid Ahmed, Rafsanjani Muhammod, Zahid Khan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 61
Deep graph learning of inter-protein contacts
Ziwei Xie, Jinbo Xu
Bioinformatics (2021) Vol. 38, Iss. 4, pp. 947-953
Open Access | Times Cited: 54
Pre-training graph neural networks for link prediction in biomedical networks
Yahui Long, Min Wu, Yong Liu, et al.
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2254-2262
Open Access | Times Cited: 52
Structure-informed Language Models Are Protein Designers
Zaixiang Zheng, Yifan Deng, Dongyu Xue, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 33
Multiple sequence alignment-based RNA language model and its application to structural inference
Yikun Zhang, Mei Lang, Jiuhong Jiang, et al.
Nucleic Acids Research (2023) Vol. 52, Iss. 1, pp. e3-e3
Open Access | Times Cited: 32
PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes
Lei Chen, Chenyu Zhang, Jing Xu
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 9
ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction
Faiza Mehmood, Shazia Arshad, Muhammad Shoaib
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 6
Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions
Daniel Bojar, Rani K. Powers, Diogo M. Camacho, et al.
Cell Host & Microbe (2020) Vol. 29, Iss. 1, pp. 132-144.e3
Open Access | Times Cited: 64
PANDA2: protein function prediction using graph neural networks
Chenguang Zhao, Tong Liu, Zheng Wang
NAR Genomics and Bioinformatics (2022) Vol. 4, Iss. 1
Open Access | Times Cited: 31
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: 29
Machine Learning Based Prediction of Enzymatic Degradation of Plastics Using Encoded Protein Sequence and Effective Feature Representation
Renjing Jiang, Lanyu Shang, Ruohan Wang, et al.
Environmental Science & Technology Letters (2023) Vol. 10, Iss. 7, pp. 557-564
Closed Access | Times Cited: 18
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