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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 in Proteomics
Bo Wen, Wen‐Feng Zeng, Yuxing Liao, et al.
PROTEOMICS (2020) Vol. 20, Iss. 21-22
Open Access | Times Cited: 127

Showing 1-25 of 127 citing articles:

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: 278
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: 226
Cancer proteogenomics: current impact and future prospects
D.R. Mani, Karsten Krug, Bing Zhang, et al.
Nature reviews. Cancer (2022) Vol. 22, Iss. 5, pp. 298-313
Closed Access | Times Cited: 147
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: 140
An Introduction to Mass Spectrometry-Based Proteomics
Steven R. Shuken
Journal of Proteome Research (2023) Vol. 22, Iss. 7, pp. 2151-2171
Closed Access | Times Cited: 112
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: 100
Advances in data‐independent acquisition mass spectrometry towards comprehensive digital proteome landscape
Reta Birhanu Kitata, Jhih‐Ci Yang, Yu‐Ju Chen
Mass Spectrometry Reviews (2022) Vol. 42, Iss. 6, pp. 2324-2348
Open Access | Times Cited: 92
Artificial Intelligence in Molecular Medicine
Bruna Gomes, Euan A. Ashley
New England Journal of Medicine (2023) Vol. 388, Iss. 26, pp. 2456-2465
Closed Access | Times Cited: 74
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: 35
AlphaPept: a modern and open framework for MS-based proteomics
Maximilian T. Strauss, Isabell Bludau, Wen‐Feng Zeng, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 34
Instrumentation at the Leading Edge of Proteomics
Trenton M. Peters-Clarke, Joshua J. Coon, Nicholas M. Riley
Analytical Chemistry (2024) Vol. 96, Iss. 20, pp. 7976-8010
Closed Access | Times Cited: 20
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: 76
Prediction of peptide mass spectral libraries with machine learning
Jürgen Cox
Nature Biotechnology (2022) Vol. 41, Iss. 1, pp. 33-43
Closed Access | Times Cited: 66
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: 14
Bacterial tolerance and detoxification of cyanide, arsenic and heavy metals: Holistic approaches applied to bioremediation of industrial complex wastes
Alfonso Olaya‐Abril, Karolina A. Biełło, G. Rodríguez-Caballero, et al.
Microbial Biotechnology (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 12
Prediction of glycopeptide fragment mass spectra by deep learning
Yi Yang, Qun Fang
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 11
Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics
Pedro Henrique Godoy Sanches, Natália Melo, Andréia M. Porcari, et al.
Biology (2024) Vol. 13, Iss. 11, pp. 848-848
Open Access | Times Cited: 11
Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning
Henry Webel, Lili Niu, Annelaura Bach Nielsen, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 8
Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks
Sian Soo Tng, Nguyen Quoc Khanh Le, Hui‐Yuan Yeh, et al.
Journal of Proteome Research (2021) Vol. 21, Iss. 1, pp. 265-273
Closed Access | Times Cited: 54
A review on recent trends in the phosphoproteomics workflow. From sample preparation to data analysis
Jiřı́ Urban
Analytica Chimica Acta (2021) Vol. 1199, pp. 338857-338857
Closed Access | Times Cited: 47
Mini-review: Recent advances in post-translational modification site prediction based on deep learning
Lingkuan Meng, Wai‐Sum Chan, Lei Huang, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 3522-3532
Open Access | Times Cited: 31
ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA
Hanyu Zhang, Yunxia Wang, Ziqi Pan, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 29
The applications of deep learning algorithms on in silico druggable proteins identification
Lezheng Yu, Xue Li, Fengjuan Liu, et al.
Journal of Advanced Research (2022) Vol. 41, pp. 219-231
Open Access | Times Cited: 28
Genome-Wide Association Study Statistical Models: A Review
Mohsen Yoosefzadeh-Najafabadi, Milad Eskandari, François Belzile, et al.
Methods in molecular biology (2022), pp. 43-62
Closed Access | Times Cited: 28
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