Rick L. Stevens

This paper addresses data imbalance in drug response prediction by introducing a multi-objective optimization approach using a custom loss function to improve model performance.

The paper evaluates computational hit-finding methods for targeting the WDR domain of LRRK2, a Parkinson's disease protein, finding that diverse strategies yielded weakly potent compounds.

GenSLMs use large language models to analyze SARS-CoV-2 genomes, enabling rapid identification of variants of concern and providing insights into evolutionary dynamics.

The paper introduces a deep learning method called Shutter Island to detect genomic islands by using image-based transfer learning, achieving better generalization than existing tools.

The paper introduces a deep learning method called Operon Hunter to detect operons in bacterial genomes by learning from visual representations of genomic data, achieving better accuracy than existing tools.

This study uses XGBoost to predict antimicrobial MICs for nontyphoidal Salmonella with high accuracy using genomic data.

Open-source KBase platform unites reference genomes and biochemistry, over 70 apps, Jupyter-based Narratives with provenance, and a Docker SDK for reproducible plant and microbial systems biology.

This study develops an XGBoost-based machine learning model to predict minimum inhibitory concentrations for 20 antibiotics in Klebsiella pneumoniae using whole genome sequencing data, achieving 92% overall accuracy.