RPSLearner: A novel approach based on random projection and deep stacking learning for categorizing NSCLC.

ABSTRACT:

Background

Lung cancer is the leading cause of cancer death, and non-small cell lung cancer (NSCLC) comprises the largest subtype with most cases. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are two NSCLC subtypes that pose challenges for accurate diagnosis using conventional methods. Existing methods are histological examination and imaging which lacks definitive histologic features and requires intense time.

Methods

To address these concerns, we propose RPSLearner, which combines Random Projection (RP) for dimensionality reduction and stacking ensemble learning to accurately predict lung cancer subtypes. Specifically, multiple independent RP matrices were first generated to project the high-dimensional RNA-seq data into lower-dimensional space, whose

SUBMITTER: Wu X 

PROVIDER: S-EPMC12247899 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature