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Sparsity is a means and not an aim in inference of gene regulatory networks
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
2010 (English)In: Proceedings of The 11th International Conference on Systems Biology (ICSB-2010), 2010Conference paper, Published paper (Refereed)
Abstract [en]

Availability of high-throughput gene expression data has lead to numerous attempts to infer network models of gene regulation based on expression changes. The low number of observations compared to the number of genes, the low signal-to-noise ratios, and the system being interampatte make the inference problem ill-posed and challenging. To solve the problem a majority of all published approaches resort to regularization, e.g. the LASSO penalty is used to find a sparse model. Regularization is known to introduce a bias, but its effect on inferred gene regulatory networks has hardly been investigated. In machine learning and compressed sensing, where regularization has been widely applied and studied, the objective is to reproduce a signal and the actual variable selection is of minor importance as long as the signal is reproduced well. In network inference, on the other hand, the variable selection is crucial since we want to identify the true topology of the network and a minimal number of links is not an aim per se. We first study the inference problem in a deterministic setting in order to gain insight and derive conditions on when the regularization causes false negative and positive links. By viewing the problem as a parameter identifiability problem, we establish three cases in which a subset of the parameters can be uniquely determined. Finally we devise conditions for invalidation of the inferred links using existing or additional data; resulting in an iterative procedure of inference and experiment design that significantly increases the confidence in the inferred network model.

Place, publisher, year, edition, pages
2010.
Keyword [en]
Gene regulatory networks, LASSO, Network inference, Regularization, Sparse networks
National Category
Control Engineering Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-80756OAI: oai:DiVA.org:kth-80756DiVA: diva2:496744
Conference
11th International Conference on Systems Biology (ICSB-2010). Edinburgh, UK. 10 Oct 2010 - 16 Oct 2010
Note

QC 20120418

Available from: 2012-02-10 Created: 2012-02-10 Last updated: 2016-08-16Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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