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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 metabolome: Applications of deep learning in metabolomics
Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 2818-2825
Open Access | Times Cited: 125

Showing 1-25 of 125 citing articles:

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: 233
Microbiome and Human Health: Current Understanding, Engineering, and Enabling Technologies
Nikhil Aggarwal, Shohei Kitano, Ginette Ru Ying Puah, et al.
Chemical Reviews (2022) Vol. 123, Iss. 1, pp. 31-72
Open Access | Times Cited: 197
New software tools, databases, and resources in metabolomics: updates from 2020
Biswapriya B. Misra
Metabolomics (2021) Vol. 17, Iss. 5
Open Access | Times Cited: 178
The metabolomics of human aging: Advances, challenges, and opportunities
Daniel J. Panyard, Bing Yu, M Snyder
Science Advances (2022) Vol. 8, Iss. 42
Open Access | Times Cited: 93
Applications of machine learning in metabolomics: Disease modeling and classification
Aya Galal, Marwa Talal, Ahmed A. Moustafa
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 90
Machine Learning in Nutrition Research
Daniel Kirk, E.J. Kok, Michele Tufano, et al.
Advances in Nutrition (2022) Vol. 13, Iss. 6, pp. 2573-2589
Open Access | Times Cited: 72
AI in analytical chemistry: Advancements, challenges, and future directions
Rafael Cardoso Rial
Talanta (2024) Vol. 274, pp. 125949-125949
Closed Access | Times Cited: 29
Metabolomics and chemometrics: The next-generation analytical toolkit for the evaluation of food quality and authenticity
Pascual García-Pérez, Pier Paolo Becchi, Leilei Zhang, et al.
Trends in Food Science & Technology (2024) Vol. 147, pp. 104481-104481
Open Access | Times Cited: 24
Machine Learning Boosts the Design and Discovery of Nanomaterials
Yuying Jia, Xuan Hou, Zhongwei Wang, et al.
ACS Sustainable Chemistry & Engineering (2021) Vol. 9, Iss. 18, pp. 6130-6147
Closed Access | Times Cited: 83
AI applications in functional genomics
Claudia Caudai, Antonella Galizia, Filippo Geraci, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 5762-5790
Open Access | Times Cited: 80
Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools
Niek De Jonge, Kevin Mildau, David Meijer, et al.
Metabolomics (2022) Vol. 18, Iss. 12
Open Access | Times Cited: 55
Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
Vivek Bhakta Mathema, Partho Sen, Santosh Lamichhane, et al.
Computational and Structural Biotechnology Journal (2023) Vol. 21, pp. 1372-1382
Open Access | Times Cited: 39
Plant and microbial sciences as key drivers in the development of metabolomics research
Asaph Aharoni, Royston Goodacre, Alisdair R. Fernie
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 12
Open Access | Times Cited: 28
Deep Learning in Image-Based Plant Phenotyping
Katherine M. Murphy, Ella Ludwig, Jorge Gutierrez, et al.
Annual Review of Plant Biology (2024) Vol. 75, Iss. 1, pp. 771-795
Closed Access | Times Cited: 10
An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles
Yongjie Deng, Yao Yao, Yanni Wang, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 10
Metabolomics and (craft) beers – recent advances
Nikko Angelo S. Carisma, Mariafe Calingacion
Food Research International (2025) Vol. 205, pp. 116010-116010
Closed Access | Times Cited: 1
DeepFeature: feature selection in nonimage data using convolutional neural network
Alok Sharma, Artem Lysenko, Keith A. Boroevich, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 55
Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders
Estela Área-Gómez, Delfina Larrea, Tianwei Yun, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 44
Defining Blood Plasma and Serum Metabolome by GC-MS
Olga I. Kiseleva, Ilya Y. Kurbatov, Ekaterina V. Ilgisonis, et al.
Metabolites (2021) Vol. 12, Iss. 1, pp. 15-15
Open Access | Times Cited: 44
AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications
Lauren Petrick, Noam Shomron
Cell Reports Physical Science (2022) Vol. 3, Iss. 7, pp. 100978-100978
Open Access | Times Cited: 36
A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction—Application in Fast Biological Spectroscopy
Zi Wang, Di Guo, Zhangren Tu, et al.
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 34, Iss. 10, pp. 7578-7592
Open Access | Times Cited: 29
Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review
Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Ahmed M. Dinar, et al.
Diagnostics (2023) Vol. 13, Iss. 4, pp. 664-664
Open Access | Times Cited: 21
Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics
Monica Thukral, Andrew E. Allen, Daniel Petras
The ISME Journal (2023) Vol. 17, Iss. 12, pp. 2147-2159
Open Access | Times Cited: 17
The potential new microbial hazard monitoring tool in food safety: Integration of metabolomics and artificial intelligence
Ying Feng, Aswathi Soni, Gale Brightwell, et al.
Trends in Food Science & Technology (2024) Vol. 149, pp. 104555-104555
Closed Access | Times Cited: 8
Multivariate analysis of NMR‐based metabolomic data
Julia Debik, Matteo Sangermani, Feng Wang, et al.
NMR in Biomedicine (2021) Vol. 35, Iss. 2
Closed Access | Times Cited: 37
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