Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes
Shuze Zhang,
Wanli Fan,
Dong He
Abstract:The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial–mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi‐omics data to develop a risk stratification model anchored on EMT‐associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT‐associated genes, employing a CoxBoost algorithm to sift thr… Show more
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