Statistical models and analyses for biological networks - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement
Communication Dans Un Congrès Année : 2011

Statistical models and analyses for biological networks

Résumé

Networks represent now an important part of post‐genomic data. This gives rise to a wide variety of statistical problems involving random graphs. In this talk, I will present recent advances made in my "Statistics for Systems Biology" group (www.ssbgroup.fr). Namely, I will present (i) how to infer gene regulatory networks from gene expression data, (ii) how to model biological networks thanks to a mixture of random graphs (MixNet/Stochastic Block Model) and to recover the latent structure, eventually allowing nodes to belong to more than one group, (iii) how to assess the significance of network motif frequencies in an observed network.
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Dates et versions

hal-02804594 , version 1 (05-06-2020)

Identifiants

  • HAL Id : hal-02804594 , version 1
  • PRODINRA : 182980

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Sophie S. Schbath. Statistical models and analyses for biological networks. Networks research cluster, Jun 2011, Oxford, United Kingdom. pp.1. ⟨hal-02804594⟩

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