Prior information-assisted integrative analysis of multiple datasets.

ABSTRACT:

Motivation

Analyzing genetic data to identify markers and construct predictive models is of great interest in biomedical research. However, limited by cost and sample availability, genetic studies often suffer from the "small sample size, high dimensionality" problem. To tackle this problem, an integrative analysis that collectively analyzes multiple datasets with compatible designs is often conducted. For regularizing estimation and selecting relevant variables, penalization and other regularization techniques are routinely adopted. "Blindly" searching over a vast number of variables may not be efficient.

Results

We propose incorporating prior information to assist integrative analysis of multiple genetic datasets. To obtain accurate prior information, we adopt a convolutio

SUBMITTER: Wang F 

PROVIDER: S-EPMC10400378 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature