GWAS with Heterogeneous Data: Estimating the Fraction of Phenotypic Variation Mediated by Gene Expression Data

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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Gene Expression Mining Guided by Background Knowledge

Data Mining and Medical Knowledge Management ◽

10.4018/978-1-60566-218-3.ch013 ◽

2011 ◽

pp. 268-292

Author(s):

Jirí Kléma ◽

Filip Železný ◽

Igor Trajkovski ◽

Filip Karel ◽

Bruno Crémilleux

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Descriptive Analysis ◽

Background Knowledge ◽

Heterogeneous Data ◽

Expression Data ◽

Heterogeneous Data Sources ◽

Biological Entities ◽

Quantitative Association Rule

This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several tasks such as relational descriptive analysis, constraintbased knowledge discovery, feature selection and construction or quantitative association rule mining. The chapter also accentuates diversity of background knowledge. In genomics, it can be stored in formats such as free texts, ontologies, pathways, links among biological entities, and many others. The authors hope that understanding of automated integration of heterogeneous data sources helps researchers to reach compact and transparent as well as biologically valid and plausible results of their gene-expression data analysis.

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

Advances in Medical Technologies and Clinical Practice - Emerging Research in the Analysis and Modeling of Gene Regulatory Networks ◽

10.4018/978-1-5225-0353-8.ch001 ◽

2016 ◽

pp. 1-51

Author(s):

David Correa Martins Jr. ◽

Fabricio Martins Lopes ◽

Shubhra Sankar Ray

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Prior Information ◽

Heterogeneous Data ◽

Data Sources ◽

Expression Data ◽

Heterogeneous Data Sources ◽

Gene Regulatory

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.

Download Full-text