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Applications of Intelligent Data Analysis for the Discovery of Gene Regulatory Networks

  • Frank Rügheimer
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 445)

Abstract

The availability of cheap sequencing and measurement techniques in molecular biology sustains a rapid increase in both quantity and quality of biomedical data. But although such methods provide detailed information about gene expression levels in samples or even individual cells, these extensive data sets merely represent snapshots of system states at given times and under a limited number of conditions. Both the high data dimensionality and low throughput sample preparation present obstacles to the identification of general mechanism underlying biological functions. This contribution documents approaches that rely on Intelligent Data Analysis (IDA) to address challenges to data analysis and interpretation in Computational Biology. Several of the documented approaches were applied within a recent interdisciplinary study dedicated to the exploration of the regulatory systems in the bacterium Bacillus subtilis, which serves as a model for several gram-positive pathogens [6, 16]

Keywords

Bayesian Network Gene Regulatory Network Association Measure Gaussian Graphical Model Bacterium Bacillus Subtilis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Biologie SystémiqueInstitut PasteurParisFrance

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