Maulik P. Shukla

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.

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 sequences 320 SARS-CoV-2 genomes from Houston to study early pandemic spread, finding diverse origins and no evidence of more virulent strains.

This study shows that machine learning can predict antimicrobial resistance using conserved non-AMR genes from incomplete genomes, achieving F1 scores of 0.80-0.89 with 100 genes.

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.