DREAM3

Network inference using dynamic context likelihood of relatedness and the inferelator

Aviv Madar, Alex Greenfield, Eric Vanden-Eijnden, Richard Bonneau

Research output: Contribution to journalArticle

Abstract

Background: Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions. Methodology: We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model. Conclusions/Significance: Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed- CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. "who regulates who" (approximately 93% of identified true positives were correctly resolved). Performance drops for high indegree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.

Original languageEnglish (US)
Article numbere9803
JournalPLoS One
Volume5
Issue number3
DOIs
StatePublished - 2010

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Genes
Time series
methodology
time series analysis
Systems Biology
Computer Simulation
Pipelines
Availability
Learning
genes
learning
Biological Sciences
prediction
testing
Data Accuracy
Datasets
gene regulatory networks

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

DREAM3 : Network inference using dynamic context likelihood of relatedness and the inferelator. / Madar, Aviv; Greenfield, Alex; Vanden-Eijnden, Eric; Bonneau, Richard.

In: PLoS One, Vol. 5, No. 3, e9803, 2010.

Research output: Contribution to journalArticle

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