A statistical framework for recovering pseudo-dynamic networks from static data.

ABSTRACT:

Motivation

The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases.

Results

We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constitu

SUBMITTER: Chen C 

PROVIDER: S-EPMC9991900 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature