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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
LICORN: learning cooperative regulation networks from gene expression data
Mohamed Elati1, Pierre Neuvial, Monique Bolotin-Fukuhara
1LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr
Bioinformatics (Oxford, England)
|August 28, 2007
Summary
This study introduces a data mining system to infer gene regulatory networks from RNA expression data. The system efficiently detects cooperative transcriptional regulation and validates findings on yeast expression datasets.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Reconstructing transcriptional regulatory networks is a key challenge in the post-genomic era.
- Understanding gene regulation requires identifying regulators and their coordination.
- DNA microarrays provide RNA expression data to study these relationships.
Purpose of the Study:
- To develop a data mining system for inferring transcriptional regulation relationships.
- To enable the detection of cooperative transcriptional regulation.
- To model regulatory relationships as labeled two-layer gene regulatory networks.
Main Methods:
- A data mining system for inferring transcriptional regulation from RNA expression values.
- Modeling regulatory relationships as labeled two-layer gene regulatory networks.
- Efficient learning of bipartite networks from discretized expression data.
Main Results:
- The proposed system is suitable for detecting cooperative transcriptional regulation.
- A method for efficient learning of bipartite networks from discretized expression data is described.
- Statistical significance of inferred networks is evaluated and methods are validated on yeast expression data.
Conclusions:
- The developed system effectively infers transcriptional regulation relationships.
- The method is capable of detecting cooperative transcriptional regulation.
- Validation on public yeast datasets supports the utility of the approach.
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