Structuring a gene network using a multiresolution independence test

In order to structure a gene network, a score-based approach is often used. A score-based approach, however, is problematic because by assuming a probability distribution, one is prevented from finding other dependent relationships with other genes. In this research, we structured a gene network from observed gene expression data using a multiresolution independence test and a conditional independence test, which is the non-parametric method proposed by Margaritis for learning the structure of Bayesian networks without making any probability distribution assumptions. The experimental results achieved an improvement in sensitivity of 0.05, and an improvement in specificity of 0.01.