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Metabolites
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19 August 2025
Metabolites | Selected Papers Published in 2023–2024 Related to Computational Metabolomics and Machine Learning


We are delighted to share some highly cited papers on computational metabolomics and machine learning research that were published in our journal Metabolites (ISSN: 2218-1989) in 2023 and 2024.

1. “Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study”
by Noya Ytzhaik, Dorit Zur, Chen Goldstein and Galit Almoznino
Metabolites 2023, 13(5), 595; https://doi.org/10.3390/metabo13050595
Available online: https://www.mdpi.com/2218-1989/13/5/595

2. “Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning”
by Tanvir Sajed, Zinat Sayeeda, Brian L. Lee, Mark Berjanskii, Fei Wang, Vasuk Gautam and David S. Wishart
Metabolites 2024, 14(5), 290; https://doi.org/10.3390/metabo14050290
Available online: https://www.mdpi.com/2218-1989/14/5/290

3. “Urinary Metabolic Distinction of Niemann–Pick Class 1 Disease through the Use of Subgroup Discovery”
by Cristóbal J. Carmona, Manuel German-Morales, David Elizondo, Victor Ruiz-Rodado and Martin Grootveld
Metabolites 2023, 13(10), 1079; https://doi.org/10.3390/metabo13101079
Available online: https://www.mdpi.com/2218-1989/13/10/1079

4. “Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy”
by Hayden Johnson and Aaryani Tipirneni-Sajja
Metabolites 2024, 14(6), 332; https://doi.org/10.3390/metabo14060332
Available online: https://www.mdpi.com/2218-1989/14/6/332

5. “An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples”
by Amy Thachil, Li Wang, Rupasri Mandal, David Wishart and Tom Blydt-Hansen
Metabolites 2024, 14(9), 474; https://doi.org/10.3390/metabo14090474
Available online: https://www.mdpi.com/2218-1989/14/9/474

6. “Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites”
by Erik D. Huckvale, Christian D. Powell, Huan Jin and Hunter N. B. Moseley
Metabolites 2023, 13(11), 1120; https://doi.org/10.3390/metabo13111120
Available online: https://www.mdpi.com/2218-1989/13/11/1120

7. “Predicting the Pathway Involvement of Metabolites Based on Combined Metabolite and Pathway Features”
by Erik D. Huckvale and Hunter N. B. Moseley
Metabolites 2024, 14(5), 266; https://doi.org/10.3390/metabo14050266
Available online: https://www.mdpi.com/2218-1989/14/5/266

8. “Predicting the Association of Metabolites with Both Pathway Categories and Individual Pathways”
by Erik D. Huckvale and Hunter N. B. Moseley
Metabolites 2024, 14(9), 510; https://doi.org/10.3390/metabo14090510
Available online: https://www.mdpi.com/2218-1989/14/9/510

9. “Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform–Infrared Spectroscopy Coupled with Machine Learning Algorithms”
by Seok-Young Kim, Seung Yong Shin, Maham Saeed, Ji Eun Ryu, Jung-Seop Kim, Junyoung Ahn, Youngmi Jung, Jung Min Moon, Chang Hwan Choi and Hyung-Kyoon Choi
Metabolites 2024, 14(1), 2; https://doi.org/10.3390/metabo14010002
Available online: https://www.mdpi.com/2218-1989/14/1/2

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