Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.

ABSTRACT:

Motivation

The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed.

Results

We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regi

SUBMITTER: Li J 

PROVIDER: S-EPMC7523642 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature