Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks.

ABSTRACT:

Background

Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.

Results

To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simu

SUBMITTER: Han B 

PROVIDER: S-EPMC3524021 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature