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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:

Deep learning the collisional cross sections of the peptide universe from a million experimental values
Florian Meier, Niklas Köhler, Andreas‐David Brunner, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 117

Showing 1-25 of 117 citing articles:

Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation
Andreas‐David Brunner, Marvin Thielert, Catherine G. Vasilopoulou, et al.
Molecular Systems Biology (2022) Vol. 18, Iss. 3
Open Access | Times Cited: 419
Artificial intelligence for proteomics and biomarker discovery
Matthias Mann, Chanchal Kumar, Wenfeng Zeng, et al.
Cell Systems (2021) Vol. 12, Iss. 8, pp. 759-770
Open Access | Times Cited: 237
Trapped Ion Mobility Spectrometry and Parallel Accumulation–Serial Fragmentation in Proteomics
Florian Meier, Melvin A. Park, Matthias Mann
Molecular & Cellular Proteomics (2021) Vol. 20, pp. 100138-100138
Open Access | Times Cited: 142
Mass Spectrometry-Based Techniques to Elucidate the Sugar Code
Márkó Grabarics, Maike Lettow, Carla Kirschbaum, et al.
Chemical Reviews (2021) Vol. 122, Iss. 8, pp. 7840-7908
Open Access | Times Cited: 130
Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF
Patricia Skowronek, Marvin Thielert, Eugenia Voytik, et al.
Molecular & Cellular Proteomics (2022) Vol. 21, Iss. 9, pp. 100279-100279
Open Access | Times Cited: 127
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
MSBooster: improving peptide identification rates using deep learning-based features
Kevin Yang, Fengchao Yu, Guo Ci Teo, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 104
Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics
Ronghui Lou, Ye Cao, Shanshan Li, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 74
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
Sensing prior constraints in deep neural networks for solving exploration geophysical problems
Xinming Wu, Jianwei Ma, Xu Si, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 23
Open Access | Times Cited: 46
Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023
Ronghui Lou, Wenqing Shui
Molecular & Cellular Proteomics (2024) Vol. 23, Iss. 2, pp. 100712-100712
Open Access | Times Cited: 40
Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF
Charlotte Adams, Wassim Gabriel, Kris Laukens, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 17
TIMS2Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore
Arthur Declercq, Robbe Devreese, Jonas Scheid, et al.
Journal of Proteome Research (2025)
Open Access | Times Cited: 2
Deep learning neural network tools for proteomics
Jesse G. Meyer
Cell Reports Methods (2021) Vol. 1, Iss. 2, pp. 100003-100003
Open Access | Times Cited: 78
A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics
Lei Xin, Rui Qiao, Xin Chen, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 70
Integration of Mass Spectrometry Data for Structural Biology
Hannah M. Britt, Tristan Cragnolini, Konstantinos Thalassinos
Chemical Reviews (2021) Vol. 122, Iss. 8, pp. 7952-7986
Open Access | Times Cited: 59
High-end ion mobility mass spectrometry: A current review of analytical capacity in omics applications and structural investigations
Daniel G. Delafield, Gaoyuan Lu, Cameron J. Kaminsky, et al.
TrAC Trends in Analytical Chemistry (2022) Vol. 157, pp. 116761-116761
Open Access | Times Cited: 52
Sensitive, High-Throughput HLA-I and HLA-II Immunopeptidomics Using Parallel Accumulation-Serial Fragmentation Mass Spectrometry
Kshiti Meera Phulphagar, Claudia Ctortecka, Alvaro Sebastian Vaca Jacome, et al.
Molecular & Cellular Proteomics (2023) Vol. 22, Iss. 6, pp. 100563-100563
Open Access | Times Cited: 30
Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models
Joseph D. Janizek, Ayse B. Dincer, Safiye Çelik, et al.
Nature Biomedical Engineering (2023) Vol. 7, Iss. 6, pp. 811-829
Closed Access | Times Cited: 28
Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics
Weiping Sun, Qianqiu Zhang, Xiyue Zhang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 24
Prediction of glycopeptide fragment mass spectra by deep learning
Yi Yang, Qun Fang
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 13
Computational tools and algorithms for ion mobility spectrometry‐mass spectrometry
Dylan H. Ross, Harsh Bhotika, Xueyun Zheng, et al.
PROTEOMICS (2024) Vol. 24, Iss. 12-13
Open Access | Times Cited: 9
An accessible workflow for high-sensitivity proteomics using parallel accumulation–serial fragmentation (PASEF)
Patricia Skowronek, Georg Wallmann, Maria Wahle, et al.
Nature Protocols (2025)
Closed Access | Times Cited: 1
Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences
Quinn Dickinson, Jesse G. Meyer
PLoS Computational Biology (2022) Vol. 18, Iss. 1, pp. e1009736-e1009736
Open Access | Times Cited: 36
A practical guide to interpreting and generating bottom‐up proteomics data visualizations
Julia P. Schessner, Eugenia Voytik, Isabell Bludau
PROTEOMICS (2022) Vol. 22, Iss. 8
Open Access | Times Cited: 34
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