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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 meets metabolomics: a methodological perspective
Partho Sen, Santosh Lamichhane, Vivek Bhakta Mathema, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 2, pp. 1531-1542
Closed Access | Times Cited: 91

Showing 1-25 of 91 citing articles:

Small molecule metabolites: discovery of biomarkers and therapeutic targets
Shi Qiu, Ying Cai, Hong Yao, et al.
Signal Transduction and Targeted Therapy (2023) Vol. 8, Iss. 1
Open Access | Times Cited: 361
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: 229
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
Machine learning meets omics: applications and perspectives
Rufeng Li, Lixin Li, Yungang Xu, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 114
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: 91
Artificial intelligence in metabolomics: a current review
Jinhua Chi, Jingmin Shu, Ming Li, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 178, pp. 117852-117852
Closed Access | Times Cited: 16
Deep learning and its applications in nuclear magnetic resonance spectroscopy
Yao Luo, Xiaoxu Zheng, Mengjie Qiu, et al.
Progress in Nuclear Magnetic Resonance Spectroscopy (2025) Vol. 146-147, pp. 101556-101556
Closed Access | Times Cited: 2
NMR: Unique Strengths That Enhance Modern Metabolomics Research
Arthur S. Edison, Maxwell B. Colonna, Gonçalo J. Gouveia, et al.
Analytical Chemistry (2020) Vol. 93, Iss. 1, pp. 478-499
Closed Access | Times Cited: 86
A new update of MALDI-TOF mass spectrometry in lipid research
Kathrin M. Engel, Patricia Prabutzki, Jenny Leopold, et al.
Progress in Lipid Research (2022) Vol. 86, pp. 101145-101145
Closed Access | Times Cited: 57
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
In-cell NMR: Why and how?
François‐Xavier Theillet, Enrico Luchinat
Progress in Nuclear Magnetic Resonance Spectroscopy (2022) Vol. 132-133, pp. 1-112
Open Access | Times Cited: 38
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: 38
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
Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine
Partho Sen, Matej Orešič
Metabolites (2023) Vol. 13, Iss. 7, pp. 855-855
Open Access | Times Cited: 24
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
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
Advances in flux balance analysis by integrating machine learning and mechanism-based models
Ankur Sahu, Mary Ann Blätke, Jędrzej Szymański, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 4626-4640
Open Access | Times Cited: 46
Analytical challenges and solutions for performing metabolomic analysis of root exudates
Mònica Escolà Casas, Víctor Matamoros
Trends in Environmental Analytical Chemistry (2021) Vol. 31, pp. e00130-e00130
Open Access | Times Cited: 45
Artificial intelligence in nutrition research: perspectives on current and future applications
Mélina Côté, Benoı̂t Lamarche
Applied Physiology Nutrition and Metabolism (2021) Vol. 47, Iss. 1, pp. 1-8
Open Access | Times Cited: 41
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
Precision Medicine Approaches with Metabolomics and Artificial Intelligence
E. Barberis, Shahzaib Khoso, Antonio Sica, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 19, pp. 11269-11269
Open Access | Times Cited: 31
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
Analytical opportunities and challenges for data handling with chemometrics strategies from LC-MS based food metabolomics
Zuojian Qin, Jie Wang, Dan Wang, et al.
Trends in Food Science & Technology (2023) Vol. 143, pp. 104298-104298
Closed Access | Times Cited: 17
Systems biology approaches to study lipidomes in health and disease
Marina Amaral Alves, Santosh Lamichhane, Alex M. Dickens, et al.
Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids (2020) Vol. 1866, Iss. 2, pp. 158857-158857
Open Access | Times Cited: 47
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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