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Related Experiment Video

Updated: Aug 11, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.

Fatma Hilal Yagin1, İpek Balikci Cicek1, Abedalrhman Alkhateeb2

  • 1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.

Computers in Biology and Medicine
|February 4, 2023
PubMed
Summary

This study introduces an explainable artificial intelligence model for diagnosing COVID-19 using gene expression data. The XGBoost model achieved high accuracy, identifying key biomarkers like IFI27 for improved clinical understanding.

Keywords:
COVID-19Explainable artificial intelligenceLIMESHAPXGBoost

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • COVID-19 diagnosis requires rapid and accurate methods.
  • Metagenomic next-generation sequencing (mNGS) offers potential for disease detection.
  • Explainable Artificial Intelligence (XAI) can enhance the interpretability of diagnostic models.

Purpose of the Study:

  • To develop and evaluate an XAI model for COVID-19 diagnosis using mNGS data.
  • To identify potential gene expression biomarkers associated with COVID-19.
  • To improve the interpretability of machine learning models in clinical diagnostics.

Main Methods:

  • Utilized a dataset of 234 patients (93 COVID-19 positive, 141 negative) with gene expression data.
  • Applied LASSO for gene selection, SVM-SMOTE for class imbalance.
  • Constructed and compared Logistic Regression, SVM, Random Forest, and XGBoost models.
  • Employed LIME and SHAP for model interpretability and biomarker identification.

Main Results:

  • The XGBoost model demonstrated superior performance with an accuracy of 0.930.
  • Identified IFI27, LGR6, and FAM83A as key genes associated with COVID-19.
  • High IFI27 gene expression was a significant predictor of positive COVID-19 diagnosis.

Conclusions:

  • The proposed XGBoost model effectively predicts COVID-19 using gene expression data.
  • Combining machine learning with LIME and SHAP provides interpretable insights into COVID-19 biomarkers.
  • This approach can assist clinicians by offering an intuitive understanding of risk factors in diagnostic models.