Author

Abhirupa Ghosh

University of Colorado AnschutzORCID 0000-0002-3980-4749Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Approach:machine learning (classical)· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Charmie K. VangiD· 2 shared Emily A BoyeriD· 2 shared Ethan P WolfeiD· 2 shared Evan Pierce BrenneriD· 2 shared Janani RaviiD· 2 shared Raymond L LesiyoniD· 2 shared Alexander McKimiD· 1 shared David A. MayeriD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersBioinformatics

Charmie K. VangORCID

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2 papers

Recent·Most cited

amR: an R package suite to predict antimicrobial resistance in bacterial pathogens

Abhirupa Ghosh, Evan Pierce Brenner, Emily A Boyer, Alexander McKim, Charmie K. Vang, Ethan P Wolfe, David A. Mayer, Raymond L Lesiyon, Janani Ravi

The paper introduces amR, an R package suite for predicting antimicrobial resistance in bacterial pathogens using genome data and interpretable machine learning models.

bioRxiv · Jul 13, 2026 · PreprintBioinformatics

From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens

Abhirupa Ghosh, Evan Pierce Brenner, Charmie K. Vang, Ethan P Wolfe, Emily A Boyer, Raymond L Lesiyon, Keenan Manpearl, Vignesh Sridhar, Joseph T Burke, Jacob D Krol, J. Bilodeaux, Janani Ravi

This paper uses machine learning to predict antimicrobial resistance (AMR) across ESKAPE pathogens by analyzing genomic data and identifying molecular features associated with drug resistance.

bioRxiv · Jul 3, 2025 · PreprintBioinformatics