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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1405332

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

Provisionally accepted
Mostafa Rezapour Mostafa Rezapour 1*Muhammad Khalid Khan Niazi Muhammad Khalid Khan Niazi 1Hao Lu Hao Lu 1Aarthi Narayanan Aarthi Narayanan 2Metin Gurcan Metin Gurcan 1
  • 1 Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, United States
  • 2 Department of Biology, George Mason University, Fairfax, Virginia, United States

The final, formatted version of the article will be published soon.

    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. Gene Ontology (GO) analysis further elucidated these genes' involvement in critical biological processes and immune response pathways, reinforcing their importance in Ebola pathogenesis. 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.

    Keywords: ebola virus infection, Gene Expression Profiling, Biomarker Discovery, Machine Learning in Virology, Transcriptomic Analysis

    Received: 22 Mar 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Rezapour, Niazi, Lu, Narayanan and Gurcan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mostafa Rezapour, Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.