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Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration

Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration

David Correa Martins Jr., Fabricio Martins Lopes, Shubhra Sankar Ray
Copyright: © 2019 |Pages: 40
ISBN13: 9781522589037|ISBN10: 1522589031|EISBN13: 9781522589044
DOI: 10.4018/978-1-5225-8903-7.ch010
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MLA

Martins Jr., David Correa, et al. "Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration." Biotechnology: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 265-304. https://doi.org/10.4018/978-1-5225-8903-7.ch010

APA

Martins Jr., D. C., Lopes, F. M., & Ray, S. S. (2019). Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration. In I. Management Association (Ed.), Biotechnology: Concepts, Methodologies, Tools, and Applications (pp. 265-304). IGI Global. https://doi.org/10.4018/978-1-5225-8903-7.ch010

Chicago

Martins Jr., David Correa, Fabricio Martins Lopes, and Shubhra Sankar Ray. "Inference of Gene Regulatory Networks by Topological Prior Information and Data Integration." In Biotechnology: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 265-304. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8903-7.ch010

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Abstract

The inference of Gene Regulatory Networks (GRNs) is a very challenging problem which has attracted increasing attention since the development of high-throughput sequencing and gene expression measurement technologies. Many models and algorithms have been developed to identify GRNs using mainly gene expression profile as data source. As the gene expression data usually has limited number of samples and inherent noise, the integration of gene expression with several other sources of information can be vital for accurately inferring GRNs. For instance, some prior information about the overall topological structure of the GRN can guide inference techniques toward better results. In addition to gene expression data, recently biological information from heterogeneous data sources have been integrated by GRN inference methods as well. The objective of this chapter is to present an overview of GRN inference models and techniques with focus on incorporation of prior information such as, global and local topological features and integration of several heterogeneous data sources.

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