Integrative gene regulatory network inference using multi-omics data Conference Paper

Authors: Zarayeneh, N.; Oh, J. H.; Kim, D.; Liu, C.; Gao, J.; Suh, S. C.; Kang, M.
Title: Integrative gene regulatory network inference using multi-omics data
Conference Title: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Abstract: Biological network inference is of importance to understand underlying biological mechanisms. Gene regulatory networks describe molecular interactions of complex biological processes. Graph models are mainly used for gene regulatory networks, where nodes and edges represent genes and their regulations respectively. In the most research, the molecular interactions (edges) of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data. However, gene expression is a product of sequential interactions of DNA sequence variations, single nucleotide polymorphism, copy number variation, histone modifications, transcription factor, DNA methylation, and many other factors. There are high-throughput genomic data that measure the various biological processes. We call the multiple types of genomics data as 'multi-omics data'. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that can incorporate multi-omics data and their interactions in the graph model of gene regulatory network. Copy number variation and DNA methylation were considered for multi-omics data in this paper. The proposed method, iGRN, was applied to the human brain data of psychiatric disorder. Through the experiments, iGRN showed its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. © 2016 IEEE.
Keywords: methylation; genes; gene expression; single nucleotide polymorphisms; dna; transcription; molecular structure; alkylation; bioinformatics; graph theory; chromosomes; gene regulatory networks; psychiatric disorders; complex networks; molecular interactions; integrative gene regulatory network inference; multi-omics data; biological mechanisms; copy number variations; dna sequence variations; sequential interactions
Journal Title Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Conference Dates: 2016 Dec 15-18
Conference Location: Shenzhen, China
ISBN: 978-1-5090-1610-5
Publisher: Institute of Electrical and Electronics Engineers Inc.  
Date Published: 2016-01-01
Start Page: 1336
End Page: 1340
Language: English
DOI: 10.1109/bibm.2016.7822711
PROVIDER: scopus
Notes: Conference Paper -- Conference code: 125905 -- Export Date: 2 March 2017 -- Harbin Institute of Technology (HIT); IEEE; IEEE Computer Society; National Science Foundation (NSF) -- 15 December 2016 through 18 December 2016 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Jung Hun Oh
    111 Oh