Author

Olatomiwa O. Bifarin

Georgia Institute of TechnologyORCID 0000-0002-5379-0881Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 3 Assay:metabolomics· 3 Approach:statistical model· 3 Research Focus:differential expression· 2 Approach:machine learning (classical)· 2 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Facundo M. FernándeziD· 2 shared Aditya Simhadri· 1 shared Varun S. Yelluru· 1 shared

Activity by Year

2023-2025 · peak 1/yr

Co-Author Collaboration Network

3 top collaborators · 3 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersScientific Communication & Education

Facundo M. FernándezORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Scientific Communication & EducationAnalytical ChemistryPeer-reviewedJul 3, 2025

✦ In-depth digest

A Large Language Model-Powered Map of Metabolomics Research

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

PubMedBERT embeddings of 80,656 metabolomics abstracts, projected with t-SNE and grouped into 20 GPT-4o mini-labelled topics, map the field's domains and trends (1998 to early 2024), browsable online.

6 citations

metabolomicstransformer / foundation model

𝕏 PostBluesky

BioinformaticsJournal of the American Society for Mass SpectrometryPeer-reviewedMay 1, 2024

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

Olatomiwa O. Bifarin, Facundo M. Fernández

This paper develops an AutoML-XAI pipeline for metabolomics data, improving cancer diagnostics by combining automated machine learning with explainable AI techniques.

50 citationstop 2%

humanmetabolomics

𝕏 PostBluesky

BioinformaticsPLoS ONEPeer-reviewedMay 4, 2023

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

Olatomiwa O. Bifarin

The paper compares SHAP and VIP for interpreting ML models in metabolomics, finding SHAP provides deeper explanations than VIP.

135 citationstop 1%

metabolomicsmachine learning (classical)

𝕏 PostBluesky