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Machine learning model for the identification of lung cancer subtypes based on dna methylation
Resource type
Authors/contributors
- Al-Qirshi, Raghad (Author)
- Basit, Syed Abdullah (Author)
- Musleh, Saleh (Author)
- Islam, Mohammad Tariqul (Author)
- Alam, Tanvir (Author)
Title
Machine learning model for the identification of lung cancer subtypes based on dna methylation
Abstract
Lung Adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two main histology subtypes of non-small cell lung cancer (NSCLC) with 70% of total Lung Cancer. In this article we proposed an ensemble-based model for the identification of subtypes of NSCLC using methylation data. Proposed Random Forest-based model along with out of bag (OOB) error based feature selection technique identified the top ten most important CpG sites that are highly differentiator between LUSC and LUAD subtypes of NSCLC with an accuracy, precision and F1 Score of \(97\%\) . The proposed model outperformed the other existing models for the same purpose with huge margin of 12%. Pathway analysis of the proposed 10 CpG sites revealed different pathways for LUAD and LUSC associated genes, LUAD-associated genes primarily participated in TP53, PTEN, GLP-1, Incretin regulation, and apoptosis. Conversely, LUSC-associated genes were predominantly involved in pathways for platelet degranulation, serine biosynthesis, and Nephrin family interaction.
Proceedings Title
Proceedings of the 2024 7th International Conference on Healthcare Service Management
Publisher
Association for Computing Machinery
Place
New York, NY, USA
Date
March 10, 2025
Pages
52–56
Series
ICHSM '24
ISBN
979-8-4007-1016-2
Citation Key
al-qirshiMachineLearningModel2025
Accessed
2025-04-24
Library Catalog
ACM Digital Library
Citation
Al-Qirshi, R., Basit, S. A., Musleh, S., Islam, M. T., & Alam, T. (2025). Machine learning model for the identification of lung cancer subtypes based on dna methylation. Proceedings of the 2024 7th International Conference on Healthcare Service Management, ICHSM ’24, 52–56. https://doi.org/10.1145/3704239.3704242
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