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First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks

Emmert-Streib, Frank and Dehmer, Matthias (2005):
First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks.
In: Proceedings of the International Conference on Enformatika, Systems Sciences and Engineering, Krakow/Poland, Enformatika 10, enformatika, pp. 65-69, [Conference or Workshop Item]

Abstract

Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.

Item Type: Conference or Workshop Item
Erschienen: 2005
Creators: Emmert-Streib, Frank and Dehmer, Matthias
Title: First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks
Language: German
Abstract:

Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.

Title of Book: Proceedings of the International Conference on Enformatika, Systems Sciences and Engineering, Krakow/Poland, Enformatika 10
Publisher: enformatika
Uncontrolled Keywords: Dynamic Bayesian networks, microarray data, structure learning, Markov chain Monte Carlo
Divisions: 20 Department of Computer Science > Telecooperation
20 Department of Computer Science
Date Deposited: 31 Dec 2016 12:59
Identification Number: ESMD:2005b
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