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Exploring the operational characteristics of inference algorithms for transcriptional networks by means of synthetic data

Koenraad Van Leemput, Tim Van den Bulcke, Thomas Dhollander, Bart De Moor, Kathleen Marchal UGent and Piet van Remortel (2008) ARTIFICIAL LIFE. 14(1). p.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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (proceedingsPaper)
publication status
published
subject
keyword
gene regulatory network, simulated data, network inference, gene expression data, GENE-EXPRESSION DATA, REGULATORY NETWORKS
journal title
ARTIFICIAL LIFE
Artif. Life
volume
14
issue
1
pages
49 - 63
conference name
8th European conference on Artificial Life
conference location
Canterbury, UK
conference start
2005-09-05
conference end
2005-09-09
Web of Science type
Article; Proceedings Paper
Web of Science id
000252248700004
JCR category
COMPUTER SCIENCE, THEORY & METHODS
JCR impact factor
1.164 (2008)
JCR rank
36/84 (2008)
JCR quartile
2 (2008)
ISSN
1064-5462
DOI
10.1162/artl.2008.14.1.49
language
English
UGent publication?
no
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3187211
handle
http://hdl.handle.net/1854/LU-3187211
date created
2013-04-08 16:30:10
date last changed
2016-12-19 15:39:25
@article{3187211,
  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.},
  author       = {Van Leemput, Koenraad and Van den Bulcke, Tim and Dhollander, Thomas and De Moor, Bart and Marchal, Kathleen and van Remortel, Piet},
  issn         = {1064-5462},
  journal      = {ARTIFICIAL LIFE},
  keyword      = {gene regulatory network,simulated data,network inference,gene expression data,GENE-EXPRESSION DATA,REGULATORY NETWORKS},
  language     = {eng},
  location     = {Canterbury, UK},
  number       = {1},
  pages        = {49--63},
  title        = {Exploring the operational characteristics of inference algorithms for transcriptional networks by means of synthetic data},
  url          = {http://dx.doi.org/10.1162/artl.2008.14.1.49},
  volume       = {14},
  year         = {2008},
}

Chicago
Van Leemput, Koenraad, Tim Van den Bulcke, Thomas Dhollander, Bart De Moor, Kathleen Marchal, and Piet van Remortel. 2008. “Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data.” Artificial Life 14 (1): 49–63.
APA
Van Leemput, K., Van den Bulcke, T., Dhollander, T., De Moor, B., Marchal, K., & van Remortel, P. (2008). Exploring the operational characteristics of inference algorithms for transcriptional networks by means of synthetic data. ARTIFICIAL LIFE, 14(1), 49–63. Presented at the 8th European conference on Artificial Life.
Vancouver
1.
Van Leemput K, Van den Bulcke T, Dhollander T, De Moor B, Marchal K, van Remortel P. Exploring the operational characteristics of inference algorithms for transcriptional networks by means of synthetic data. ARTIFICIAL LIFE. 2008;14(1):49–63.
MLA
Van Leemput, Koenraad, Tim Van den Bulcke, Thomas Dhollander, et al. “Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data.” ARTIFICIAL LIFE 14.1 (2008): 49–63. Print.