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Feature context-dependency and complexity-reduction in probability landscapes for integrative genomics.
1Institut des Hautes Etudes Scientifiques, Bures-sur-Yvette, France. lesne@ihes.fr
Theoretical Biology & Medical Modelling
|September 12, 2008
Summary
Probability landscapes offer a systematic method to analyze context-dependent gene regulation. This approach simplifies complexity and provides insights into eukaryotic gene regulatory codes for genome-wide analysis.
Area of Science:
- Systems Biology
- Genomics
- Bioinformatics
Background:
- Integrating heterogeneous biological data for eukaryotic gene regulation is a major systems biology challenge.
- Probability landscapes offer a framework for analyzing correlations in genomic data.
- Functional genomics information is context-dependent, necessitating flexible discovery methods.
Purpose of the Study:
- To systematically investigate feature context-dependency using probability landscapes.
- To explore methods for reducing the computational complexity of probability landscapes.
- To link complexity reduction to the analysis of context-dependency.
Main Methods:
- Utilizing probability landscapes to analyze feature context-dependency.
- Conditionally collapsing feature probability profiles.
- Developing a systematic approach for discovering context-dependency in functional genomics data.
Main Results:
- Demonstrated systematic investigation of feature context-dependency via probability landscapes.
- Showcased conditional collapsing of feature probability profiles to reduce complexity.
- Linked complexity reduction directly to the analysis of context-dependency.
Conclusions:
- Advances in probability landscapes simplify cross-correlation analysis for hypothesis testing.
- Gained insights into biological gene regulatory problems and feature classification.
- Provided a basis for novel mathematical structures for eukaryotic gene regulation.
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