Constructing gene regulatory networks from microarray data using non-Gaussian pair-copula Bayesian networks

Omid Chatrabgoun, Amin Hosseinian-Far, Alireza Daneshkhah

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)
    26 Downloads (Pure)


    Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality, multi-modality and heavy tailedness that are commonly seen in current massive genetic data. To relax this limitative constraint, one can apply copula function which is a multivariate cumulative distribution function with uniform marginal distribution. However, since the dependency structures of different pairs of genes in a multivariate problem are very different, the regular multivariate copula will not allow for the construction of an appropriate model. The solution to this problem is using Pair-Copula Constructions (PCCs) which are decompositions of a multivariate density into a cascade of bivariate copula, and therefore, assign different bivariate copula function for each local term. In fact, in this paper, we have constructed inverse covariance matrix based on the use of PCCs when the normality assumption can be moderately or severely violated for capturing a wide range of distributional features and complex dependency structure. To learn the non-Gaussian model for the considered GRN with non-Gaussian genomic data, we apply modified version of copula-based PC algorithm in which normality assumption of marginal densities is dropped. This paper also considers the Dynamic Time Warping (DTW) algorithm to determine the existence of a time delay relation between two genes. Breast cancer is one of the most common diseases in the world where GRN analysis of its subtypes is considerably important; Since by revealing the differences in the GRNs of these subtypes, new therapies and drugs can be found. The findings of our research are used to construct GRNs with high performance, for various subtypes of breast cancer rather than simply using previous models.

    Original languageEnglish
    Article number2050023
    Number of pages21
    JournalJournal of Bioinformatics and Computational Biology
    Issue number4
    Publication statusPublished - 24 Jul 2020

    Bibliographical note

    Electronic version of an article published as Journal of Bioinformatics and Computational Biology, vol. 18, no. 4, 2050023. © copyright World Scientific Publishing Company

    Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.


    • Gene regulatory networkaphical modelsdynamic time warping algorithmmodified PC algorithmpair-copula constructions
    • Gaussian graphical models
    • Gene regulatory networks
    • dynamic time warping algorithm
    • pair-copula constructions
    • modified PC algorithm

    ASJC Scopus subject areas

    • Molecular Biology
    • Biochemistry
    • Computer Science Applications


    Dive into the research topics of 'Constructing gene regulatory networks from microarray data using non-Gaussian pair-copula Bayesian networks'. Together they form a unique fingerprint.

    Cite this