Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.

ABSTRACT: This study aimed to identify candidate diagnostic miRNAs from the serum of colorectal cancer (CRC) patients using Boruta, a wrapper-based feature selection technique, in combination with decision tree-based machine learning methods. We analyzed three serum miRNA expression profile datasets from the gene expression omnibus (GEO) database to identify differentially expressed miRNAs common to both cancerous and non-cancerous samples. The GSE106817 dataset, comprising 2568 miRNAs, was used to train our models. The Boruta machine learning feature selection method was applied to identify robust and significant miRNAs associated with CRC in the training cohort. Next, random forest and XGBoost models were trained using the selected miRNAs. To validate the predictive efficacy of the identified cand

SUBMITTER: Hamidi F 

PROVIDER: S-EPMC12500991 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature