A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.

ABSTRACT:

Purpose

Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM.

Methods

The study proposes a deep learning framework combining early fusion feature autoencoder (AE) and late fusion feature AE for risk subtype prediction of SKCM. The deep learning framework integrates mRNA, miRNA, and DNA methylation data of SKCM patients from The Cancer Genome Atlas (TCGA), and clusters the screened multi-omics features associated with survival prognosis to identify risk subtypes. Differential expression analysis and functional enrichment analysis were performed between risk subtypes, while SVM classifiers were construc

SUBMITTER: Li W 

PROVIDER: S-EPMC11796813 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature