High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data.

ABSTRACT: Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dat

SUBMITTER: Amniouel S 

PROVIDER: S-EPMC10830836 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature