Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning.

ABSTRACT: Cancer is a highly heterogeneous disease characterized by complex molecular changes. Subtypes identified through multi-omics data hold significant promise for improving prognosis and facilitating personalized precision treatment. Recent multi-omics integration methods have mostly focused on capturing complementary information from different data types, often overlooking potential interactions between omics data. Here we develop a novel method named interactive multi-kernel learning (iMKL), which incorporates omics-omics interactions alongside heterogeneous data types under the unsupervised multi-kernel learning framework, to improve subtype identification. Using the sample-similarity kernel for each dataset, we propose a joint Hadamard product strategy to capture higher-order interactive e

SUBMITTER: Cao H 

PROVIDER: S-EPMC12710476 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature