‡ Joint first authors

Figure abstract

A Large Language Model-Powered Map of Metabolomics Research

Olatomiwa O. Bifarin, Varun S. Yelluru, Aditya Simhadri, Facundo M. Fernández

Analytical Chemistry, 2025

📄 Paper 📝 Preprint 🌐 Web App

A comprehensive analysis of metabolomics research trends using large language models to map the evolution and future directions of the field.

Figure abstract

Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics

Olatomiwa O. Bifarin, and Facundo M. Fernández

Journal of the American Society for Mass Spectrometry, 2024

📄 Paper 📝 Preprint

Development of automated machine learning pipelines with explainable AI for enhancing cancer biomarker discovery in metabolomics datasets.

Figure abstract

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

Samyukta Sah‡, Olatomiwa O Bifarin‡, Samuel G Moore, David A. Gaul, Hyewon Chung, Hanbyoul Cho, Chi-Heum Cho, Jae-Hoon Kim, Jaeyeon Kim, Facundo O Fernandez

Cancer Epidemiology, Biomarkers & Prevention, 2024

📄 Paper 📝 Preprint

Provisional patent was filed for the findings from this work.
Identification of serum lipid biomarkers for ovarian cancer detection using advanced metabolomics and machine learning approaches.

Figure abstract

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Olatomiwa O Bifarin‡, Samyukta Sah‡, David A Gaul, Samuel G Moore, Ruihong Chen, Murugesan Palaniappan, Jaeyeon Kim, Martin M Matzuk, Facundo M Fernández

Journal of Proteome Research, 2023

📄 Paper 📝 Preprint 🎥 Video Abstract

Comprehensive analysis of lipid metabolism changes during ovarian cancer progression using machine learning and multi-platform metabolomics.

Figure abstract

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification

Olatomiwa O Bifarin

PLOS ONE, 2023

📄 Paper 📝 Preprint 🎥 Video Abstract

Development and validation of interpretable machine learning methods for metabolomics data analysis with focus on clinical applications.

Figure abstract

Urine-Based Metabolomics, and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

Bifarin, O.O.; Gaul, D.A.; Sah, S.; Arnold, R.S.; Ogan, K.; Master, V.A.; Roberts, D.L.; Bergquist, S.H.; Petros, J.A.; Edison, A.S.; Fernández, M.F.

Cancers, 2021

📄 Paper

Investigation of urine metabolites associated with different stages of renal cell carcinoma using machine learning approaches.

Figure abstract

Machine Learning-enabled Renal Cell Carcinoma Status Prediction Using Multi-Platform Urine-based Metabolomics

Olatomiwa O. Bifarin‡, David A. Gaul‡, Samyukta Sah, Rebecca S. Arnold, Kenneth Ogan, Viraj A. Master, David L. Roberts, Sharon H. Bergquist, John A. Petros, Facundo M. Fernández, Arthur S. Edison

Journal of Proteome Research, 2021

📄 Paper 🌐 Web App

Non-invasive detection and stratification of renal cell carcinoma using urine metabolomics and machine learning approaches.

Figure abstract

A Genome-Wide Screen with Nicotinamide to Identify Sirtuin-Dependent Pathways in Saccharomyces cerevisiae

John S. Choy, Bayan Qadri, Leah Henry, Kunal Shroff, Olatomiwa Bifarin, and Munira A. Basrai

G3 (Bethesda), 2016

📄 Paper

Systematic investigation of sirtuin-dependent pathways in yeast using genome-wide screening approaches.