A new Bayesian piecewise linear regression model for dynamic network reconstruction.

ABSTRACT:

Background

Linear regression models are important tools for learning regulatory networks from gene expression time series. A conventional assumption for non-homogeneous regulatory processes on a short time scale is that the network structure stays constant across time, while the network parameters are time-dependent. The objective is then to learn the network structure along with changepoints that divide the time series into time segments. An uncoupled model learns the parameters separately for each segment, while a coupled model enforces the parameters of any segment to stay similar to those of the previous segment. In this paper, we propose a new consensus model that infers for each individual time segment whether it is coupled to (or uncoupled from) the previous segment.

Resu

SUBMITTER: Shafiee Kamalabad M 

PROVIDER: S-EPMC8074473 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature