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This study introduces the Supervised Magnitude-Altitude Scoring (SMAS)methodology, a machine learning-based approach, for analyzing gene expressiondata obtained from nonhuman primates (NHPs) infected with Ebola virus (EBOV).We utilize a comprehensive dataset of NanoString gene expression profiles fromEbola-infected NHPs, deploying the SMAS system for nuanced host-pathogeninteraction analysis. SMAS effectively combines gene selection based onstatistical significance and expression changes, employing linear classifierssuch as logistic regression to accurately differentiate between RT-qPCRpositive and negative NHP samples. A key finding of our research is theidentification of IFI6 and IFI27 as critical biomarkers, demonstratingexceptional predictive performance with 100% accuracy and Area Under the Curve(AUC) metrics in classifying various stages of Ebola infection. Alongside IFI6and IFI27, genes, including MX1, OAS1, and ISG15, were significantlyupregulated, highlighting their essential roles in the immune response to EBOV.Our results underscore the efficacy of the SMAS method in revealing complexgenetic interactions and response mechanisms during EBOV infection. Thisresearch provides valuable insights into EBOV pathogenesis and aids indeveloping more precise diagnostic tools and therapeutic strategies to addressEBOV infection in particular and viral infection in general.

Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates