Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 20, 2023

Abstract

BACKGROUND AND OBJECTIVE: The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and quality of life. Being a heterogeneous disease at the molecular and cellular level, the biomarkers responsible for the heterogeneity aid in distinguishing NSCLC into its prominent subtypes-adenocarcinoma and squamous cell carcinoma. Moreover, if identified, these biomarkers could pave the path to targeted therapy. Through this work, a novel explainable AI (XAI)-guided deep learning framework is proposed that assists in discovering a set of significant NSCLC-relevant biomarkers using methylation data.

Authors

  • Kountay Dwivedi
    Department of Computer Science, University of Delhi, Delhi, India. Electronic address: kountaydwivedi@gmail.com.
  • Ankit Rajpal
    Department of Computer Science, University of Delhi, Delhi, India. Electronic address: arajpal@cs.du.ac.in.
  • Sheetal Rajpal
    Department of Computer Science, Dyal Singh College, Delhi, India. Electronic address: sheetal.rajpal.09@gmail.com.
  • Virendra Kumar
    Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India. Electronic address: virendrakumar@aiims.edu.
  • Manoj Agarwal
    Department of Computer Science, Hans Raj College, University of Delhi, Delhi, India. Electronic address: agar.manoj@gmail.com.
  • Naveen Kumar
    National Centre for Veterinary Type Cultures, ICAR-National Research Centre on Equines, Hisar, India.

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