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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:

The application of artificial neural networks in metabolomics: a historical perspective
Kevin Mendez, David Broadhurst, Stacey N. Reinke
Metabolomics (2019) Vol. 15, Iss. 11
Closed Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

Machine Learning Applications for Mass Spectrometry-Based Metabolomics
Ulf W. Liebal, An Phan, Malvika Sudhakar, et al.
Metabolites (2020) Vol. 10, Iss. 6, pp. 243-243
Open Access | Times Cited: 255
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: 89
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: 83
Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
Quoc Bao Pham, Sk Ajim Ali, Farhana Parvin, et al.
Advances in Space Research (2024) Vol. 74, Iss. 1, pp. 17-47
Closed Access | Times Cited: 14
A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification
Kevin Mendez, Stacey N. Reinke, David Broadhurst
Metabolomics (2019) Vol. 15, Iss. 12
Open Access | Times Cited: 141
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: 122
Recent trends in application of chemometric methods for GC-MS and GC×GC-MS-based metabolomic studies
Neda Feizi, Fatemeh Sadat Hashemi-Nasab, Fatemeh Golpelichi, et al.
TrAC Trends in Analytical Chemistry (2021) Vol. 138, pp. 116239-116239
Closed Access | Times Cited: 95
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: 90
NMR signal processing, prediction, and structure verification with machine learning techniques
Carlos Cobas
Magnetic Resonance in Chemistry (2020) Vol. 58, Iss. 6, pp. 512-519
Closed Access | Times Cited: 85
Metabolomics: A useful tool for ischemic stroke research
Wentao Li, Chongyu Shao, Chang Li, et al.
Journal of Pharmaceutical Analysis (2023) Vol. 13, Iss. 9, pp. 968-983
Open Access | Times Cited: 23
Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach
Gabriele Santilli, Mario Vetrano, Massimiliano Mangone, et al.
Life (2024) Vol. 14, Iss. 6, pp. 681-681
Open Access | Times Cited: 7
Recent applications of chemometrics in one‐ and two‐dimensional chromatography
Tijmen S. Bos, Wouter C. Knol, Stef R.A. Molenaar, et al.
Journal of Separation Science (2020) Vol. 43, Iss. 9-10, pp. 1678-1727
Open Access | Times Cited: 62
Chemometric applications in metabolomic studies using chromatography-mass spectrometry
Alessandra Paul, Peter de B. Harrington
TrAC Trends in Analytical Chemistry (2020) Vol. 135, pp. 116165-116165
Closed Access | Times Cited: 60
MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra
Aditya Shrivastava, Neil Swainston, Soumitra Samanta, et al.
Biomolecules (2021) Vol. 11, Iss. 12, pp. 1793-1793
Open Access | Times Cited: 52
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: 40
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: 20
Artificial neural network detection of pancreatic cancer from proton (1H) magnetic resonance spectroscopy patterns of plasma metabolites
Meiyappan Solaiyappan, Santosh Kumar Bharti, Raj Kumar Sharma, et al.
Communications Medicine (2025) Vol. 5, Iss. 1
Open Access
Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks
Kevin Mendez, David Broadhurst, Stacey N. Reinke
Metabolomics (2020) Vol. 16, Iss. 2
Open Access | Times Cited: 46
Data mining/machine learning methods in foodomics
Ana M. Jiménez‐Carvelo, Luis Cuadros-Rodrı́guez
Current Opinion in Food Science (2020) Vol. 37, pp. 76-82
Closed Access | Times Cited: 41
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: 37
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
Forecasting copper price by application of robust artificial intelligence techniques
Hasel Amini Khoshalan, Jamshid Shakeri, Iraj Najmoddini, et al.
Resources Policy (2021) Vol. 73, pp. 102239-102239
Closed Access | Times Cited: 34
Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning
Alexandre de Fátima Cobre, Mônica Surek, Dile Pontarolo Stremel, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105659-105659
Open Access | Times Cited: 22
Authentication of apples (Malus × domestica Borkh.) according to geographical origin, variety and production method using 1H-NMR spectroscopy and random forest
Soeren Wenck, René Bachmann, Sarah‐Marie Barmbold, et al.
Food Control (2024) Vol. 167, pp. 110817-110817
Open Access | Times Cited: 4
Application of untargeted volatile profiling and data driven approaches in wine flavoromics research
María Pérez‐Jiménez, Emma Sherman, María Ángeles Pozo‐Bayón, et al.
Food Research International (2021) Vol. 145, pp. 110392-110392
Closed Access | Times Cited: 27
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