Ed Seabolt

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.

The paper analyzes RNA sequencing data from respiratory tract samples to identify functional differences in microbiomes between COVID-19 patients, pneumonia patients, and healthy controls.