[논문]앙상블 기법을 활용한 RNA-Sequencing 데이터의 폐암 예측 연구

Geon AN; JooYong PARK

doi:10.24225/jkaia.2024.2.1.7

[국내논문] 앙상블 기법을 활용한 RNA-Sequencing 데이터의 폐암 예측 연구
A Study on Predicting Lung Cancer Using RNA-Sequencing Data with Ensemble Learning 원문보기

Journal of Korea Artificial Intelligence Association, v.2 no.1, 2024년, pp.7 - 14

Geon AN (Department of Medical IT, Eulji University) , JooYong PARK (Department of Big Data Medical Convergence, Eulji University)

Abstract ▼ AI-Helper

In this paper, we explore the application of RNA-sequencing data and ensemble machine learning to predict lung cancer and treatment strategies for lung cancer, a leading cause of cancer mortality worldwide. The research utilizes Random Forest, XGBoost, and LightGBM models to analyze gene expression profiles from extensive datasets, aiming to enhance predictive accuracy for lung cancer prognosis. The methodology focuses on preprocessing RNA-seq data to standardize expression levels across samples and applying ensemble algorithms to maximize prediction stability and reduce model overfitting. Key findings indicate that ensemble models, especially XGBoost, substantially outperform traditional predictive models. Significant genetic markers such as ADGRF5 is identified as crucial for predicting lung cancer outcomes. In conclusion, ensemble learning using RNA-seq data proves highly effective in predicting lung cancer, suggesting a potential shift towards more precise and personalized treatment approaches. The results advocate for further integration of molecular and clinical data to refine diagnostic models and improve clinical outcomes, underscoring the critical role of advanced molecular diagnostics in enhancing patient survival rates and quality of life. This study lays the groundwork for future research in the application of RNA-sequencing data and ensemble machine learning techniques in clinical settings.

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표/그림 (3)

그림 Figure 1: Scatter Plot of -log10(P-value)
표 Table 1: Optimized hyperparameters for each algorithm
그림 Figure 2: Gene importance using LIME

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