William Andreopoulos

This paper develops a machine learning model to classify riboswitch performance for detecting Coxsackievirus B3, showing strong accuracy and computational efficiency.

ASTA improves spatial transcriptomics analysis through clustering, annotation, and AI-driven hypothesis generation, achieving significant performance gains and biological insights.

This paper evaluates two mechanistic interpretability methods on a protein language model to identify features distinguishing Class A beta-lactamase from other classes.

The paper compares metagenomic assembly tools and machine learning models for organism identification, finding that ensembling improves performance for organisms with specific characteristics.

Deeplasmid uses deep learning to accurately distinguish plasmids from bacterial chromosomes in assembled genomes, achieving high precision and validating novel plasmid detection.