Publications

Chemical space separation: Scaffold and UMAP splits are closer to random than we think. Bryn Marie Reimer, Vy Do, Anna G. Green (2026). In ChemRxiv.

FARM: Forecasting Antibiotic Resistance in Mycobacterium tuberculosis using biophysics and machine learning. Mahbuba Tasmin, Shrishti Barethiya, Yu Wang, et al. (2026). In bioRxiv.

Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning. Irene Lepori, Zichen Liu, Nelson Evbarunegbe, et al. (2026). In Nature Microbiology.

ActiveFusion: Fused representations improve active learning for molecular property prediction. Nelson Evbarunegbe, Shiyun Wa, Luke Taylor, et al. (2026). In JCIM.

A Frameshift Mutation in the Methyltransferase rlmN Is Associated with Increased Linezolid Resistance in Mycobacterium tuberculosis. Bryn Marie Reimer, Anna G. Green (2026). In CSBJ.

Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response. Sanjana G. Kulkarni, Anna G. Green, Brendon C. Mann, et al. (2026). In Nature Communications.

MycoPermeNet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student Self-Distillation. Nelson Evbarunegbe, Shiyun Wa, Isha Karn, et al. (2026). In JCIM.

BIG-TB: A benchmark for prediction and interpretability of sequence-based machine learning using Mycobacterium tuberculosis genomes. Mahbuba Tasmin, Saishradha Mohanty, Sanjana Kulkarni, et al. (2026). In bioRxiv.

The structural context of mutations in proteins predicts their effect on antibiotic resistance. Anna G. Green, Mahbuba Tasmin, Roger Vargas Jr, et al. (2025). In bioRxiv.

ESCMID workshop: Artificial intelligence and machine learning in medical microbiology diagnostics. Mariella Greutmann, Karsten Borgwardt, Sarah Brüningk, et al. (2025). In Microbes and Infection.

Mechanistic evidence that motif-gated domain recognition drives contact prediction in protein language models. Jatin Nainani, Bryn Marie Reimer, Connor Watts, et al. (2025). In bioRxiv.

Transcriptomic responses to antibiotic exposure in Mycobacterium tuberculosis. Husain Poonawala, Yu Zhang, Sravya Kuchibotla, et al. (2024). In JAAC.

Analysis of Genome-Wide Mutational Dependence in Naturally Evolving Mycobacterium tuberculosis Populations. Anna G. Green, Roger Vargas Jr, Maximillian G. Marin, et al. (2023). In MBE.

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis. Anna G. Green, Chang Ho Yoon, Michael L. Chen, et al. (2022). In Nature Communications.