MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) is a highly heterogeneous cancer type with a poor prognosis. Accurate subtype identification can help guide its treatment. The traditional subtype identification methods using a single-omics approach make it difficult to comprehensively characterize the molecular features of LUAD. Identification of subtypes through multi-omics association strategies can effectively supplement the shortcomings of single-omics information.

Methods

In this study, we used the Generative Adversarial Network (GAN) to mine transcriptomic, proteomic, and epigenomic information and generate an integrated data set. The newly integrated data were then used to identify LUAD immune subtypes. In the improved GAN (MOGAN) method, we not only integrated multiple omics da

SUBMITTER: He H 

PROVIDER: S-EPMC11868734 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature