James H. Kaufman

The paper introduces macroscopic metrics to analyze RNA-seq data and reveal chromatin modifications in aging and disease, showing their sensitivity to chromatin structure changes and effectiveness in distinguishing cell states.

This paper predicts virulence in Klebsiella pneumoniae using expanded gene function discoveries and machine learning, achieving higher accuracy than traditional methods.

The paper predicts epitope candidates for SARS-CoV-2 by analyzing epitope sequences from related coronaviruses and identifying conserved and diverse antigen regions across 62k genomes.

The paper develops a semi-supervised pipeline to annotate SARS-CoV-2 genomes, identifying molecular targets with high accuracy and scalability.

The paper analyzes how SARS-CoV-2 mutations affect RT-PCR primer specificity and finds a 2% monthly increase in mismatches and geographic-specific virus detection.

This paper analyzes antimicrobial resistance assay data to identify biases and contradictions in training data for machine learning models.