Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing.

ABSTRACT:

Background

In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded.

Methods

We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to

SUBMITTER: Kojima K 

PROVIDER: S-EPMC3587380 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature