Unveiling Immune Response Mechanisms in Mpox Infection Through Machine Learning Analysis of Time Series Gene Expression Data

Abstract

Monkeypox virus (Mpox) has recently drawn global attention due to outbreaks beyond its traditional endemic regions. Understanding the immune response to Mpox infection is essential for improving disease management and guiding vaccine development. In this study, we used several machine learning algorithms to analyze time series gene expression data from macaques infected with Mpox, aiming to uncover key immune-related genes involved in different stages of infection. The dataset covered early infection, late infection, and rechallenge phases. We applied nine feature ranking methods to analyze the feature importance, obtaining nine feature lists. Then, the incremental feature selection method was applied to each list to extract key genes and build efficient prediction models and classification rules for each list. This procedure employed twelve classification algorithms and the Synthetic Minority Oversampling Technique. Key genes—such as CD19, MS4A1, and TLR10—were repeatedly identified from multiple feature lists, and are known to play vital roles in B-cell activation, antibody production, and innate immunity. Furthermore, we identified several novel key genes (HS3ST1, SPAG16, and MTARC2) that have not been reported previously. These findings offer valuable insights into the host immune response and highlight potential molecular targets for monitoring and intervention in Mpox infections.

Keywords

• monkeypox virus
• immune
• machine learning
• classification rule