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.

这项研究引入了基于机器学习的监督型振幅 - 高度评分（SMAS）方法，用于分析非人灵长类动物（NHPs）感染埃博拉病毒（EBOV）的基因表达数据。我们利用一组全面的 NanoString 基因表达谱数据集，采用 SMAS 系统进行微妙的宿主 - 病原互作分析。我们的研究的一个关键发现是确定了 IFI6 和 IFI27 作为关键生物标志物，展示了 100% 准确率和曲线下面积（AUC）度量值在不同阶段的埃博拉感染分类方面的卓越预测性能。除 IFI6 和 IFI27 外，基因 MX1、OAS1 和 ISG15 明显上调，凸显了它们在免疫应答中对 EBOV 的重要作用。我们的研究结果强调了 SMAS 方法在揭示 EBOV 感染期间复杂遗传相互作用和应答机制方面的功效。这项研究为 EBOV 的发病机制提供了有价值的见解，并有助于开发更精确的诊断工具和治疗策略，以针对埃博拉感染和其他病毒感染。