Title: Integration of multi-omics data for integrative gene regulatory network inference

Authors: Neda Zarayeneh; Euiseong Ko; Jung Hun Oh; Sang Suh; Chunyu Liu; Jean Gao; Donghyun Kim; Mingon Kang

Addresses: Department of Computer Science, Texas A&M University Commerce, Commerce, TX, USA ' Department of Computer Science, Kennesaw State University, Marietta, GA, USA ' Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA ' Department of Computer Science, Texas A&M University Commerce, Commerce, TX, USA ' Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA ' Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA ' Department of Computer Science, Kennesaw State University, Marietta, GA, USA ' Department of Computer Science, Kennesaw State University, Marietta, GA, USA

Abstract: Gene regulatory networks provide comprehensive insights and in-depth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Keywords: gene regulatory network inference; multi-omics data; data integration.

DOI: 10.1504/IJDMB.2017.087178

International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.3, pp.223 - 239

Available online: 03 Oct 2017 *

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