Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.

ABSTRACT: Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering. MLMF initially processes multi-omics feature matrices by performing multi-layer linear or nonlinear factorization, decomposing the original data into latent feature representations unique to each omics

SUBMITTER: Ren Y 

PROVIDER: S-EPMC12418959 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature