Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.

Journal: Computers in biology and medicine
PMID: 36738712

Abstract

AIM: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples.

Authors

  • Fatma Hilal Yagin
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye.
  • İpek Balikci Cicek
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey. Electronic address: ipek.balikci@inonu.edu.tr.
  • Abedalrhman Alkhateeb
    School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, N9B 3P4, ON, Canada. alkhate@uwindsor.ca.
  • Burak Yagin
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey. Electronic address: burak.yagin@inonu.edu.tr.
  • Cemil Colak
    Inonu University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Malatya, Turkey. Electronic address: cemilcolak@yahoo.com.
  • Mohammad Azzeh
    Department of Software Engineering, Applied Science Private University, P.O. Box 166, Amman, Jordan.
  • Sami Akbulut
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey; Inonu University, Faculty of Medicine, Department of Surgery, 44280, Malatya, Turkey; Inonu University, Faculty of Medicine, Department of Public Health, 44280, Malatya, Turkey. Electronic address: akbulutsami@gmail.com.

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