machine-learning

Genotype-phenotype modeling of light ecotypes in Prochlorococcus reveals genomic signatures of ecotypic divergence

Prochlorococcus is a cyanobacterial genus that exhibits photosynthetic capacity and remarkable genetic diversity. We analyze how Prochlorococcus genomics relate to high vs. low light environment adaptations, applying traditional comparative genomics …

From sequence to signature: Uncovering multiscale AMR features across bacterial pathogens with supervised machine learning

Since the clinical introduction of antibiotics in the 1940s, antimicrobial resistance (AMR) has become an increasingly dire threat to global public health. Pathogens acquire AMR much faster than we discover new drugs (antibiotics), warranting …

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

EB and AG are co-primary authors.

Machine learning & comparative genomics for antimicrobial resistance

Integrative machine learning to predict AMR and characterize pathogen genomics across clinical and environmental contexts.