This paper is in the following e-collection/theme issue:

Theme Issue 2025: Artificial Intelligence in Oncology (10) AI applications in genomics and pathology informatics, including ‘Generative AI’ (21) Lung Cancer (68) Machine Learning (2124) Precision Medicine and Genetics (82)

Published on in Vol 7 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70553, first published .
Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non–Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study

Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non–Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study

Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non–Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study

Authors of this article:

Rayan Mubarak1 Author Orcid Image ;   Fahim Islam Anik2 Author Orcid Image ;   Jean T Rodriguez3 Author Orcid Image ;   Nazmus Sakib4 Author Orcid Image ;   Mohammad A Rahman3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Rayan Mubarak   1 ;   Fahim Islam Anik   2 , MS ;   Jean T Rodriguez   3 , MS ;   Nazmus Sakib   4 , PhD ;   Mohammad A Rahman   3

1 Cypress Bay High School, Weston, FL, United States

2 Department of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh

3 School of Computing and Information Sciences, Florida International University, Miami, FL, United States

4 Department of Information Technology, Kennesaw State University, Marietta, GA, United States

Corresponding Author:

  • Nazmus Sakib, PhD
  • Department of Information Technology
  • Kennesaw State University
  • Atrium Building J3218, 1100 South Marietta Pkwy SE
  • Marietta, GA 30067
  • United States
  • Phone: 1 4147975981
  • Email: nsakib1@kennesaw.edu