Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Sanguinetti, Guido
6 Issues per year
IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104
CiteScore 2017: 0.86
SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527
Mathematical Citation Quotient (MCQ) 2017: 0.04
An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data
Gene regulatory networks refer to the interactions that occur among genes and other cellular products. The topology of these networks can be inferred from measurements of changes in gene expression over time. However, because the measurement device (i.e., microarrays) typically yields information on thousands of genes over few biological replicates, these systems are quite difficult to elucidate. An approach with proven effectiveness for inferring networks is the Dynamic Bayesian Network. We have developed an iterative empirical Bayesian procedure with a Kalman filter that estimates the posterior distributions of network parameters. We compare our method to similar existing methods on simulated data and real microarray time series data. We find that the proposed method performs comparably on both model-based and data-based simulations in considerably less computational time. The R and C code used to implement the proposed method are publicly available in the R package ebdbNet.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.