Reverse Engineering Gene Regulatory Networks Using Graph Mining

Haodi Jiang, Turki Turki, Sen Zhang, Jason T.L. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages335-349
Number of pages15
ISBN (Print)9783319961354
DOIs
StatePublished - Jan 1 2018
Event14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 - New York, United States
Duration: Jul 15 2018Jul 19 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10934 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
Country/TerritoryUnited States
CityNew York
Period7/15/187/19/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Applications in biology and medicine
  • Graph mining
  • Network inference
  • Pattern discovery

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