Richard Slayden

Oct 14, 2024 | biorxiv.org | Richard Slayden |Michael Kirby |Ugochukwu O. Ugwu

AbstractThis paper develops optimization and Machine Learning (ML) algorithms to analyze gene expression datasets from the lungs and spleen of mice, infected intranasally, with two bacterial strains, Francisella tularensis - Schu4 and Live Vaccine Strain (LVS). We propose and utilize Weighted l-norm Generalized Eigenvalue-type Problems (l1-WGEPs) to determine a small set of host biomarkers that report Schu4 and LVS infection of the lungs and dissemination to the spleen.