  This study introduces the Supervised Magnitude-Altitude Scoring (SMAS)
methodology, a machine learning-based approach, for analyzing gene expression
data obtained from nonhuman primates (NHPs) infected with Ebola virus (EBOV).
We utilize a comprehensive dataset of NanoString gene expression profiles from
Ebola-infected NHPs, deploying the SMAS system for nuanced host-pathogen
interaction analysis. SMAS effectively combines gene selection based on
statistical significance and expression changes, employing linear classifiers
such as logistic regression to accurately differentiate between RT-qPCR
positive and negative NHP samples. A key finding of our research is the
identification of IFI6 and IFI27 as critical biomarkers, demonstrating
exceptional predictive performance with 100% accuracy and Area Under the Curve
(AUC) metrics in classifying various stages of Ebola infection. Alongside IFI6
and IFI27, genes, including MX1, OAS1, and ISG15, were significantly
upregulated, highlighting their essential roles in the immune response to EBOV.
Our results underscore the efficacy of the SMAS method in revealing complex
genetic interactions and response mechanisms during EBOV infection. This
research provides valuable insights into EBOV pathogenesis and aids in
developing more precise diagnostic tools and therapeutic strategies to address
EBOV infection in particular and viral infection in general.
