scholarly journals Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

2018 ◽  
Vol 1 (1) ◽  
pp. 138-148
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Human Gene ◽  
Expression Data ◽  
Research Questions ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Context Specific ◽  
Genome Scale ◽  
Network Project

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

scholarly journals Accurate differential analysis of transcription factor activity from gene expression

2018 ◽  
Author(s):  
Viren Amin ◽  
Murat Can Cobanoglu
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Differential Analysis ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene ◽  
Perturbation Estimation

AbstractWe present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)

scholarly journals Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Central Problem ◽  
Expression Data ◽  
Redundancy Reduction ◽  
Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

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

Biotechnology ◽  
2019 ◽  
pp. 265-304
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.

scholarly journals Identification of context-specific gene regulatory networks with GEMULA—gene expression modeling using LAsso

2011 ◽  
Vol 28 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Geert Geeven ◽  
Ronald E. van Kesteren ◽  
August B. Smit ◽  
Mathisca C. M. de Gunst
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Context Specific
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