Sophie Lèbre (english version)

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I moved to Montpellier, please find my new home page here.

--- Course in Practical Systems Biology: R package G1DBN in practice ---

Research Interests

  • Graphical models, Dynamic Bayesian networks, Genetic networks inference.
  • Bayesian inference, MCMC algorithms, Information sharing.
  • Gene evolution models with insertion, deletion and substitution
  • Markov models, HMM, Mixture Transition Distribution (MTD) models.


Complete list (+bibtex file)

    • Journals
      • Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure
        F. Dondelinger, S. Lèbre, D. Husmeier, Machine Learning, Juillet 2012, [Article]
      • Wisdom of crowds for robust gene network inference
        D. Marbach et al., Nature Methods, 2012 [Article]
      • Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series
        F. Dondelinger, D. Husmeier and S. Lebre, Euphytica, 2012, 183 (3), 361-377. [Article] [Preprint]
      • Statistical inference of the time-varying structure of gene-regulation networks
        S. Lèbre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, BMC Systems Biology, 2010, 4:130. [Article]
      • Inferring dynamic bayesian network with low order independencies
        S. Lèbre, Statistical Applications in Genetics and Molecular Biology, 2009: Vol. 8: Iss. 1, Article 9. [Article] [Supplementary Material] [Preprint]
      • An EM algorithm for estimation in the Mixture Transition Distribution Model
        S. Lèbre and P-Y. Bourguignon, Journal of Statistics Computation and Simulation, 2008: Vol. 78 Iss. 8, 713. [Article] [Preprint]

    • Refereed Conference Proceedings
      • Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks
        D. Husmeier, F. Dondelinger, S. Lèbre. Proceedings of the The Neural Information Processing Systems NIPS 2010.
      • Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
        F. Dondelinger, S. Lèbre, D. Husmeier. Proceedings of the International Conference on Machine Learning (ICML) 2010.

    • Book
      • Bayesian Networks in R with Applications in Systems Biology
        R. Nagarajan, M. Scutari, S. Lèbre, Springer-Verlag Use R series, To appear.

    • Book Chapters
      • Nonhomogeneous Dynamic Bayesian Networks in Systems Biology
        S. Lèbre, F. Dondelinger, D. Husmeier, In Next Generation Microarray Bioinformatics : Methods and Protocols
        (J. Wang, A. C. C. Tan, T. Tian) Series: Methods in Molecular Biology, 2012, Vol. 802, Chapitre 13, pp. 199-213. [Book] [Chapter ]
      • Recovering Genetic Network from Continuous Data with Dynamic Bayesian Networks
        G. Lelandais and S. Lèbre, In Handbook of Statistical Systems Biology (eds M. P. H. Stumpf, D. J. Balding and M. Girolami), John Wiley & Sons, Ltd, Chichester, UK, 2011, Chapter 12 [Book][Chapter ].
      • Modeling a Regulatory Network Using Temporal Gene Expression Data: Why and How?
        S. Lèbre and G. Lelandais, In Automation in Genomics and Proteomics: An Engineering Case-Based Approach: MIT and Harvard interdisciplinary special studies courses (R. Benson, G. Alterovitz and M. Ramoni, ed.), March 2009, Chapter 4. [Book]

    • PhD Thesis: Stochastic process analysis for Genomics and Dynamic Bayesian Networks inference

    • Journals
      • Genome Evolution by Transformation, Expansion and Contraction (GETEC)
        E. Benard, S. Lèbre, C. J. Michel, Biosystems, 2015, 135, 15–34. [Article]
      • An Evolution Model for Sequence Length Based on Residue Insertion–Deletion Independent of Substitution: An Application to the GC Content in Bacterial Genomes
        S. Lèbre, C. J. Michel, Bulletion of Mathematical Biology, 2012, Vol. 74, Number 8, pp. 1764-1788. [Article]
      • A stochastic evolution model for residue Insertion-Deletion Independent from Substitution (IDIS)
        S. Lèbre, C. J. Michel, Computational Biology and Chemistry, 2010, Vol. 34, Iss. 5-6, pp. 259-267. [Article]
    • Poster

Computer software

  • EDISON : Estimation of Directed Interactions from Sequences Of Nonhomogeneous gene expression, extension of the R package ARTIVA . An algorithm implemented in R which runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty. [Dondelinger et al., 2012].
  • ARTIVA : An algorithm implemented in R for time varying Dynamic Bayesian Networks inference based on an Auto Regressive TIme VArying model [Lèbre et al., 2010].
  • G1DBN: R package performing Dynamic Bayesian Networks inference. Aims at recovering genetic regulatory networks when the number of genes is much higher than the number of time points. Freely available from the R archive CRAN [Lèbre, 2009].


Personal Links


Sophie Lèbre
Unversity of Strasbourg
ICube - UMR7357
Pôle API
Bd Sébastien Brant - BP 10413
67412 Illkirch cedex
Tel : +33 (0)3 
email : sophie.lebre @