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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Artificial Intelligence Approach in Machine Learning-Based Modeling and Networking of the Coronavirus Pathogenesis

Shihori Tanabe1, Sabina Quader2, Ryuichi Ono3

  • 1Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki 210-9501, Japan.

Current Issues in Molecular Biology
|July 23, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models predict coronavirus pathogenesis pathway activation in SARS-CoV-2 infection. This approach using pathway images may help identify effective treatments for severe acute respiratory syndrome coronavirus 2.

Keywords:
artificial intelligencecoronaviral infectioncoronavirusmachine learningmolecular networkmolecular pathway imagenetwork analysispathway analysisprediction model

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Area of Science:

  • Virology
  • Computational Biology
  • Bioinformatics

Background:

  • The coronavirus pathogenesis pathway, involving signaling cascades like interferon and apoptosis, is activated during severe acute respiratory syndrome (SARS) coronavirus infection.
  • Understanding these pathways is crucial for developing targeted therapies against coronaviruses, including SARS-CoV-2.

Purpose of the Study:

  • To develop and validate machine learning models for predicting the activation states of the coronavirus pathogenesis pathway.
  • To analyze SARS-CoV-2 infection data using Ingenuity Pathway Analysis (IPA) to identify relevant pathways and cell types.

Main Methods:

  • Utilized artificial intelligence and machine learning on images of the coronavirus pathogenesis pathway.
  • Analyzed over 100,000 SARS-CoV-2 related datasets from the IPA database.
  • Developed a prediction model in Python 3.11 using pathway images from infected human-induced pluripotent stem cells (iPSCs) and lung adenocarcinoma (LUAD) cells.

Main Results:

  • The coronavirus pathogenesis pathway was found to be activated in SARS-CoV-2-infected iPSC-derived cells and LUAD cells.
  • Machine learning models were successfully developed to predict pathway activation states.
  • Identified 27 relevant analyses, including those involving iPSC-derived cells and LUAD.

Conclusions:

  • The developed prediction model for coronavirus pathogenesis pathway activation states shows potential for aiding in treatment identification for SARS-CoV-2 infections.
  • This AI-driven approach offers a novel method for analyzing complex viral pathogenesis pathways.