Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.

ABSTRACT:

Motivation

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges.

Results

We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its

SUBMITTER: Hai Y 

PROVIDER: S-EPMC10627352 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature