Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival
Roman Jaksik,
Kamila Szumała,
Khanh Ngoc Dinh
et al.
Abstract:Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease’s complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CP… Show more
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