Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma.

ABSTRACT: Lung cancer is the second most common cancer in the United States and the leading cause of mortality in cancer patients. Biomarkers predicting survival of patients with lung cancer have a profound effect on patient prognosis and treatment. However, predictive biomarkers for survival and their relevance for lung cancer are not been well known yet. The objective of this study was to perform machine learning with data from The Cancer Genome Atlas of patients with lung adenocarcinoma (LUAD) to find survival-specific gene mutations that could be used as survival-predicting biomarkers. To identify survival-specific mutations according to various clinical factors, four feature selection methods (information gain, chi-squared test, minimum redundancy maximum relevance, and correlation) were used.

SUBMITTER: Cho HJ 

PROVIDER: S-EPMC6231670 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature