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Supervised Inference of Gene Regulatory Networks from Positive and Unlabeled Examples

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Data Mining for Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 939))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Duke University, Durham, NC, USA

    Fantine Mordelet

  2. Mines ParisTech, Centre for Computational Biology, Fontainebleau, France

    Jean-Philippe Vert

Authors
  1. Fantine Mordelet
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  2. Jean-Philippe Vert
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Corresponding author

Correspondence to Fantine Mordelet .

Editor information

Editors and Affiliations

  1. , Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan

    Hiroshi Mamitsuka

  2. Dept. Biomedical Engineering, Boston University, Cummington St. 44, Boston, 02215, Massachusetts, USA

    Charles DeLisi

  3. Inst. Chemical Research, Bioinformatics Center, Kyoto University, Gokasho, Uji, 611-0011, Kyoto, Japan

    Minoru Kanehisa

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Mordelet, F., Vert, JP. (2013). Supervised Inference of Gene Regulatory Networks from Positive and Unlabeled Examples. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_5

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  • DOI: https://doi.org/10.1007/978-1-62703-107-3_5

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-106-6

  • Online ISBN: 978-1-62703-107-3

  • eBook Packages: Springer Protocols

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