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:

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: 105

Showing 1-25 of 105 citing articles:

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: 99

MS-Based Proteomics of Body Fluids: The End of the Beginning
Jakob M. Bader, Vincent Albrecht, Matthias Mann
Molecular & Cellular Proteomics (2023) Vol. 22, Iss. 7, pp. 100577-100577
Open Access | Times Cited: 68

Robust dimethyl‐based multiplex‐DIA doubles single‐cell proteome depth via a reference channel
Marvin Thielert, Corazon Ericka Mae M. Itang, Constantin Ammar, et al.
Molecular Systems Biology (2023) Vol. 19, Iss. 9
Open Access | Times Cited: 60

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

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: 39

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

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 Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
Ibomoiye Domor Mienye, Nobert Jere
IEEE Access (2024) Vol. 12, pp. 96893-96910
Open Access | Times Cited: 24

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

Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition
Patricia Skowronek, Florian Krohs, Markus Lubeck, et al.
Molecular & Cellular Proteomics (2022) Vol. 22, Iss. 2, pp. 100489-100489
Open Access | Times Cited: 45

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit
Mario Picciani, Wassim Gabriel, Victor Giurcoiu, et al.
PROTEOMICS (2023) Vol. 24, Iss. 8
Open Access | Times Cited: 36

Leveraging transformers‐based language models in proteome bioinformatics
Nguyen Quoc Khanh Le
PROTEOMICS (2023) Vol. 23, Iss. 23-24
Closed Access | Times Cited: 35

A large-scale proteogenomic atlas of pear
Peng Wang, Xiao Wu, Zebin Shi, et al.
Molecular Plant (2023) Vol. 16, Iss. 3, pp. 599-615
Open Access | Times Cited: 23

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design
Xinyi Wu, Huitian Lin, Renren Bai, et al.
European Journal of Medicinal Chemistry (2024) Vol. 268, pp. 116262-116262
Closed Access | Times Cited: 13

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

Machine Learning in Soft Matter: From Simulations to Experiments
Kaihua Zhang, Xiangrui Gong, Ying Jiang
Advanced Functional Materials (2024) Vol. 34, Iss. 24
Closed Access | Times Cited: 9

Koina: Democratizing machine learning for proteomics research
Ludwig Lautenbacher, Kevin Yang, Tobias Kockmann, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 9

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: 8

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: 1

Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation
Pradeep Kumar Yadalam, Deepavalli Arumuganainar, Prabhu Manickam Natarajan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1

Translation dysregulation in cancer as a source for targetable antigens
Chen Weller, Osnat Bartok, Christopher S. McGinnis, et al.
Cancer Cell (2025)
Open Access | Times Cited: 1

Ionmob: a Python package for prediction of peptide collisional cross-section values
David Teschner, David Gomez‐Zepeda, Arthur Declercq, et al.
Bioinformatics (2023) Vol. 39, Iss. 9
Open Access | Times Cited: 19

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery
Charlotte Stutzmann, Jiaxi Peng, Zhaoguan Wu, et al.
Cell Reports Methods (2023) Vol. 3, Iss. 6, pp. 100511-100511
Open Access | Times Cited: 18

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing
Tom Kazmirchuk, Calvin Bradbury-Jost, Taylor Ann Withey, et al.
Genes (2023) Vol. 14, Iss. 6, pp. 1194-1194
Open Access | Times Cited: 17

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