WMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

ABSTRACT:

Background

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

Methods

We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision.

Results

wMKL models each data type with multiple kernel choices, thus alleviating the sensit

SUBMITTER: Cao H 

PROVIDER: S-EPMC10951206 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature