Faculty Collaboration Database

A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data. J Comput Biol 2015 Apr;22(4):289-99

Date

04/07/2015

Pubmed ID

25844668

DOI

10.1089/cmb.2014.0296

Scopus ID

2-s2.0-84926655923 (requires institutional sign-in at Scopus site)   18 Citations

Abstract

One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

Author List

Baur B, Bozdag S

Author

Serdar Bozdag BS,PhD Assistant Professor, Director of Bioinformatics Lab in the Dept. of Mathematics, Statistics and Computer Science department at Marquette University

MESH terms used to index this publication - Major topics in bold

Algorithms
Area Under Curve
Bayes Theorem
Breast Neoplasms
DNA Methylation
Epigenesis, Genetic
Female
Gene Expression
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Models, Genetic
ROC Curve