Title: Exploring the operational characteristics of inference algorithms for transcriptional networks by means of synthetic data
Authors: Van Leemput, Koenraad * ×
Van den Bulcke, Tim *
Dhollander, Thomas
De Moor, Bart
Marchal, Kathleen
van Remortel, Piet #
Issue Date: 2008
Publisher: MIT Press
Series Title: Artificial Life vol:14 issue:1 pages:49-63
Abstract: The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and associated expression data. This article reports the application of SynTReN, an existing network generator that samples topologies from existing biological networks and uses Michaelis-Menten and Hill enzyme kinetics to simulate gene interactions. We illustrate the effects of different aspects of the expression data on the quality of the inferred network. The tested expression data parameters are network size, network topology, type and degree of noise, quantity of expression data, and interaction types between genes. This is done by applying three well-known inference algorithms to SynTReN data sets. The results show the power of synthetic data in revealing operational characteristics of inference algorithms that are unlikely to be discovered by means of biological microarray data only.
ISSN: 1064-5462
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Centre of Microbial and Plant Genetics
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
* (joint) first author
× corresponding author
# (joint) last author

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