Reverse Engineering Gene Regulatory Networks Using Graph Mining

Document Type

Conference Proceeding

Publication Date

1-1-2018

Abstract

Reverse engineering gene regulatory networks (GRNs), also known as GRN inference, refers to the process of reconstructing GRNs from gene expression data. A GRN is modeled as a directed graph in which nodes represent genes and edges show regulatory relationships between the genes. By predicting the edges to infer a GRN, biologists can gain a better understanding of regulatory circuits and functional elements in cells. Many bioinformatics tools have been developed to computationally reverse engineer GRNs. However, none of these tools is able to perform perfect GRN inference. In this paper, we propose a graph mining approach capable of discovering frequent patterns from the GRNs inferred by existing methods. These frequent or common patterns are more likely to occur in true regulatory networks. Experimental results on different datasets demonstrate the good quality of the discovered patterns, and the superiority of our approach over the existing GRN inference methods.

Identifier

85050541451 (Scopus)

ISBN

[9783319961354]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-319-96136-1_27

e-ISSN

16113349

ISSN

03029743

First Page

335

Last Page

349

Volume

10934 LNAI

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