Reference : Predicting missing expression values in gene regulatory networks using a discrete log...
Scientific journals : Article
Human health sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/16449
Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states
English
Crespo, Isaac mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Krishna, Abhimanyu mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Le Béchec, Antony [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
del Sol Mesa, Antonio mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
*Co-first-authors [> >]
2013
Nucleic Acids Research
Oxford University Press
41
1
e8
Yes (verified by ORBilu)
0305-1048
1362-4962
Oxford
United Kingdom
[en] The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions.
http://hdl.handle.net/10993/16449
10.1093/nar/gks785
* These authors contributed equally to this work

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