PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exploring the independence of gene regulatory modules
Janne Nikkilä, Antti Honkela and Samuel Kaski
In: Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB 2006), 17-18 June 2006, Tuusula, Finland.

Abstract

We study the discovery of gene regulatory modules based on transcription factor (TF) binding data and expression data from gene knockouts. We invoke the natural assumption that regulatory modules predominantly operate independently, which makes it possible to apply a new method for extracting them: the Independent Variable Group Analysis. We demonstrate that i) the independence assumption helps in discovering the regulatory modules from TF data, and ii) the independent gene modules discovered from TF-data can be found also in expression data from gene knockouts. This demonstrates that the regulatory effects by transcription factors are observable in knockout experiments. It additionally suggests that the difficult interpretation of the knockout experiments could be eased by taking into account the independent regulatory modules.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:2602
Deposited By:Antti Honkela
Deposited On:22 November 2006