Supervised Inference of Gene Regulatory Networks from Positive and Unlabeled Examples

  • Fantine Mordelet
  • Jean-Philippe Vert
Part of the Methods in Molecular Biology book series (MIMB, volume 939)


Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell’s working mechanisms. We present SIRENE, a method to estimate a GRN from a collection of expression data. Contrary to most existing methods for GRN inference, SIRENE requires as input a list of known regulations, in addition to expression data, and implements a supervised machine-learning approach based on learning from positive and unlabeled examples to account for the lack of negative examples.

Key words

Gene regulatory network Reverse engineering Inference Machine learning Gene expression 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceDuke UniversityDurhamUSA
  2. 2.Mines ParisTechCentre for Computational BiologyFontainebleauFrance

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