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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 neural network tools for proteomics
Jesse G. Meyer
Cell Reports Methods (2021) Vol. 1, Iss. 2, pp. 100003-100003
Open Access | Times Cited: 78

Showing 1-25 of 78 citing articles:

Glycoproteomics
Ieva Bagdonaite, Stacy A. Malaker, Daniel A. Polasky, et al.
Nature Reviews Methods Primers (2022) Vol. 2, Iss. 1
Open Access | Times Cited: 157
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: 35
AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
Tomasz Wasilewski, Wojciech Kamysz, Jacek Gębicki
Biosensors (2024) Vol. 14, Iss. 7, pp. 356-356
Open Access | Times Cited: 31
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: 24
Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review
Liangtao Lai, Yuansheng Liu, Bosheng Song, et al.
ACM Computing Surveys (2025)
Closed Access | Times Cited: 4
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: 69
Positive-unlabeled learning in bioinformatics and computational biology: a brief review
Fuyi Li, Shuangyu Dong, André Leier, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 57
Artificial neural networks in contemporary toxicology research
Igor Pantić, Jovana Paunović, Jelena Cumic, et al.
Chemico-Biological Interactions (2022) Vol. 369, pp. 110269-110269
Closed Access | Times Cited: 54
Photoaffinity labelling with small molecules
Rick A. Homan, John D. Lapek, Christina M. Woo, et al.
Nature Reviews Methods Primers (2024) Vol. 4, Iss. 1
Closed Access | Times Cited: 10
Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors into Peptide Identification
Mostafa Kalhor, Joel Lapin, Mario Picciani, et al.
Molecular & Cellular Proteomics (2024) Vol. 23, Iss. 7, pp. 100798-100798
Open Access | Times Cited: 9
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
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art
Abubaker Abdelrahman, Serestina Viriri
Journal of Imaging (2022) Vol. 8, Iss. 3, pp. 55-55
Open Access | Times Cited: 37
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
pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level
Siyuan Kong, Pengyun Gong, Wen‐Feng Zeng, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 30
Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review
Cheuk Chi Albert Ng, Yin Zhou, Zhongping Yao
Analytica Chimica Acta (2023) Vol. 1268, pp. 341330-341330
Closed Access | Times Cited: 22
Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps
Paula Carrillo-Rodríguez, Frode Selheim, Maria Hernandez-Valladares
Cancers (2023) Vol. 15, Iss. 2, pp. 555-555
Open Access | Times Cited: 19
Machine learning for the advancement of genome-scale metabolic modeling
Pritam Kundu, Satyajit Beura, Suman Mondal, et al.
Biotechnology Advances (2024) Vol. 74, pp. 108400-108400
Closed Access | Times Cited: 7
Towards chemoenzymatic labeling strategies for profiling protein glycosylation
Yinping Tian, Shengzhou Ma, Liuqing Wen
Current Opinion in Chemical Biology (2024) Vol. 80, pp. 102460-102460
Closed Access | Times Cited: 6
AlphaPept, a modern and open framework for MS-based proteomics
Maximilian T. Strauss, Isabell Bludau, Wen‐Feng Zeng, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Closed Access | Times Cited: 35
ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics
Tobias Greisager Rehfeldt, Ralf Gabriels, Robbin Bouwmeester, et al.
Journal of Proteome Research (2023) Vol. 22, Iss. 2, pp. 632-636
Open Access | Times Cited: 15
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
Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer
Md Zahirul Islam Khan, Shing Yau Tam, Hkw Law
Cells (2022) Vol. 11, Iss. 6, pp. 973-973
Open Access | Times Cited: 20
ADH-PPI: An attention-based deep hybrid model for protein-protein interaction prediction
Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Muhammad Imran Malik, et al.
iScience (2022) Vol. 25, Iss. 10, pp. 105169-105169
Open Access | Times Cited: 20
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 13
Advances, obstacles, and opportunities for machine learning in proteomics
Heather Desaire, Eden P. Go, David Hua
Cell Reports Physical Science (2022) Vol. 3, Iss. 10, pp. 101069-101069
Open Access | Times Cited: 18
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