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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:

Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities
Markus Ekvall, Patrick Truong, Wassim Gabriel, et al.
Journal of Proteome Research (2022) Vol. 21, Iss. 5, pp. 1359-1364
Open Access | Times Cited: 22

Showing 22 citing articles:

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics
Wen‐Feng Zeng, Xie‐Xuan Zhou, Sander Willems, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 106
Toward an Integrated Machine Learning Model of a Proteomics Experiment
Benjamin A. Neely, Viktoria Dorfer, Lennart Martens, et al.
Journal of Proteome Research (2023) Vol. 22, Iss. 3, pp. 681-696
Open Access | Times Cited: 48
Recent Developments in Machine Learning for Mass Spectrometry
Armen G. Beck, Matthew Muhoberac, Caitlin E. Randolph, et al.
ACS Measurement Science Au (2024) Vol. 4, Iss. 3, pp. 233-246
Open Access | Times Cited: 19
Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry
Yonghan Yu, Ming Li
Nature Machine Intelligence (2025)
Closed Access | Times Cited: 2
Leveraging transformers‐based language models in proteome bioinformatics
Nguyen Quoc Khanh Le
PROTEOMICS (2023) Vol. 23, Iss. 23-24
Closed Access | Times Cited: 36
Alternative proteoforms and proteoform-dependent assemblies in humans and plants
Claire D. McWhite, Wisath Sae-Lee, Yaning Yuan, et al.
Molecular Systems Biology (2024) Vol. 20, Iss. 8, pp. 933-951
Open Access | Times Cited: 5
An Interpretable Hybrid Deep Learning Model for Molten Iron Temperature Prediction at the Iron-Steel Interface Based on Bi-LSTM and Transformer
Zhenzhong Shen, Weigang Han, Yanzhuo Hu, et al.
Mathematics (2025) Vol. 13, Iss. 6, pp. 975-975
Open Access
Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models
Jesse Angelis, Eva Ayla Schröder, Zixuan Xiao, et al.
PROTEOMICS (2025)
Open Access
Application of LLMs/Transformer-Based Models for Metabolite Annotation in Metabolomics
Yijiang Liu, Feifan Zhang, Ying Ge, et al.
(2025), pp. 7-7
Closed Access
Implementing N-terminomics and machine learning to probe in vivo Nt-arginylation
Cheolju Lee, Shinyeong Ju, Laxman Nawale, et al.
Research Square (Research Square) (2025)
Closed Access
Machine learning‐based peptide‐spectrum match rescoring opens up the immunopeptidome
Charlotte Adams, Kris Laukens, Wout Bittremieux, et al.
PROTEOMICS (2023) Vol. 24, Iss. 8
Open Access | Times Cited: 9
Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics
Ceder Dens, Charlotte Adams, Kris Laukens, et al.
Journal of the American Society for Mass Spectrometry (2024) Vol. 35, Iss. 9, pp. 2143-2155
Open Access | Times Cited: 2
AlphaPeptDeep: A modular deep learning framework to predict peptide properties for proteomics
Wen‐Feng Zeng, Xie‐Xuan Zhou, Sander Willems, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 10
A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics
Thang V. Pham, Vinh Van Nguyen, Duong Vu, et al.
PROTEOMICS (2023) Vol. 23, Iss. 7-8
Open Access | Times Cited: 5
AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence
Lewis Y. Geer, Joel Lapin, Douglas J. Slotta, et al.
Journal of Proteome Research (2023) Vol. 22, Iss. 7, pp. 2246-2255
Open Access | Times Cited: 4
Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics
Ceder Dens, Charlotte Adams, Kris Laukens, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles
Mehdi Bagheri Hamaneh, Aleksey Y. Ogurtsov, O. I. Obolensky, et al.
Journal of Proteome Research (2024) Vol. 23, Iss. 6, pp. 1983-1999
Open Access | Times Cited: 1
DMSS: An Attention-Based Deep Learning Model for High-Quality Mass Spectrometry Prediction
Yihui Ren, Yu Wang, Wenkai Han, et al.
Big Data Mining and Analytics (2024) Vol. 7, Iss. 3, pp. 577-589
Open Access | Times Cited: 1
Research on the Upper Limit of Accuracy for Predicting Theoretical Tandem Mass Spectrometry
Changjiu He, Xiaoyu Wang, Mingming Lyu, et al.
Journal of Computer and Communications (2024) Vol. 12, Iss. 03, pp. 184-195
Open Access
Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition
Ka Yang, Joao A. Paulo, Steven P. Gygi, et al.
Journal of the American Society for Mass Spectrometry (2024)
Closed Access
Protein aggregation capture assisted profiling of the thiol redox proteome
Ana Martínez‐Val, Samuel Lozano-Juárez, Jorge Lumbreras, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
UniSpec: A Deep Learning Approach for Predicting Energy-Sensitive Peptide Tandem Mass Spectra and Generating Proteomics-Wide In-Silico Spectral Libraries
Joel Lapin, Xinjian Yan, Qian Dong
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
Peptidomics Methods Applied to the Study of Flower Development
Raquel Álvarez-Urdiola, Eva Borràs, Federico Valverde, et al.
Methods in molecular biology (2023), pp. 509-536
Closed Access
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