Title
Identifying Dynamical Time Series Model Parameters From Equilibrium Samples, With Application to Gene Regulatory Networks
Publication Date
8-1-2018
Document Type
Article
Abstract
Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver. © 2018, SAGE Publications.
Publication Title
Statistical Modelling
DOI
10.1177/1471082X18776577
Publisher Policy
pre print, post print (12 month embargo)
Recommended Citation
Young, W.C.; Yeung, K.Y.; and Raftery, A.E., "Identifying Dynamical Time Series Model Parameters From Equilibrium Samples, With Application to Gene Regulatory Networks" (2018). School of Engineering and Technology Publications. 198.
https://digitalcommons.tacoma.uw.edu/tech_pub/198
