A new insight into underlying disease mechanism through semi-parametric latent differential network model.

ABSTRACT:

Background

In genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.

Results

We propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive e

SUBMITTER: He Y 

PROVIDER: S-EPMC6309076 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature