Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

M. de Campos Luis, Cano Andrés, Castellano Javier G. () and Moral Serafín
Additional contact information
M. de Campos Luis: Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Cano Andrés: Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Castellano Javier G.: Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Moral Serafín: Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 3, 14

Abstract: Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.

Keywords: Bayesian networks; genetic regulatory networks; microarray data; prior knowledge; structural restrictions (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2018-0042 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:18:y:2019:i:3:p:14:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2018-0042

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().