Published online 28 November 2016
Nucleic Acids Research, 2017, Vol. 45, No. 4 2221
doi: 10.1093/nar/gkw1160
Erratum
A prior-based integrative framework for functional
transcriptional regulatory network inference
Alireza F. Siahpirani1 and Sushmita Roy2,3,*
1
Department of Computer Sciences, University of Wisconsin-Madison, 1210 W. Dayton St. Madison, WI 53706-1613,
USA, 2 Wisconsin Institute for Discovery, University of Wisconsin-Madison, Discovery Building 330 North Orchard St.
Madison, WI 53715, USA and 3 Department of Biostatistics and Medical Informatics, University of
Wisconsin-Madison, K6/446 Clinical Sciences Center 600 Highland Avenue Madison, WI 53792–4675, USA
Nucl. Acids Res. (2016) doi: 10.1093/nar/gkw963
The arrows in Figure 1 were missed during the art conversion process. A new Figure 1 is provided below. The Editors and
Publisher apologise to the Authors and Readers for this error and inconvenience caused.
Figure 1. Overview of our approach to integrate diverse regulatory genomic datasets as structure priors. Xi and X j denote a regulator (such as a transcription factor) and a target gene respectively. For each candidate edge Xi → X j , different sources of prior networks can be used. The figure shows three
different types of prior networks: ChIP, Motif and Knockout. Each prior network can be weighted, for example wi,Mj denotes the weight of the regulatory
edge in the motif network. Different prior networks are combined to specify the prior probability of an edge using a single logistic function with prior parameters ␤0 , ␤M , ␤K , ␤C . ␤◦ controls for network sparsity, while the other parameters specify importance of the prior networks. The regulatory network is
itself represented as a dependency network learned by estimating a set of conditional probability distributions. In the example, the conditional distribution
of the target gene X4 is specified by three regulators, X1 , X2 , X3 . denotes a particular form of conditional probability distributions that is parameterized
by .
* To
whom correspondence should be addressed. Tel: +1 608 316 4453; Fax: +1 608 316 4604; Email: [email protected]
C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which
permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
[email protected]