MLSB08, the Second International Workshop on Machine Learning in Systems Biology will be held in Brussels on September 13-14 2008 in the Palace of the Royal Academy of Belgium.

The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to complex biological and medical questions by bringing together method developers and experimentalists.


Molecular biology and also all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines/tools sharing the "-omics" suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type.
The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First of all, the amount of available experimental data is not at all a limiting factor any more; on the contrary, there is a plethora of it. The challenge has shifted towards identifying the relevant pieces of information given the question, and how to make sense out of it (a "data mining" issue). Secondly, rather than to focus on components in isolation, we can now try to understand how biological systems behave as the result of the integration and interaction between the individual components that one can now monitor simultaneously (so called "systems biology").
Taking advantage of this wealth of "genomic" information has become a conditio sine qua non for whoever ambitions to remain competitive in molecular biology and more generally in biomedical sciences. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms.

Scientific Program Committee

  • Florence d'Alché-Buc (University of Evry, France)
  • Christophe Ambroise (University of Evry, France)
  • Pierre Geurts (University of Liège, Belgium)
  • Mark Girolami (University of Glasgow, UK)
  • Samuel Kaski (University of Helsinki, Finland)
  • Kathleen Marchal (Katholieke Universiteit Leuven, Belgium)
  • Elena Marchiori (Vrije Universiteit Amsterdam, The Netherlands)Yves Moreau (Katholieke Universiteit Leuven, Belgium)
  • Gunnar Rätsch (FML, Max Planck Society, Tübingen)
  • Juho Rousu (University of Helsinki, Finland)
  • Céline Rouveirol (University of Paris XIII, France)
  • Yvan Saeys (University of Gent, Belgium)
  • Rodolphe Sepulchre (University of Liège, Belgium)
  • Koji Tsuda (Max Planck Institute, Tuebingen)
  • Jacques Van Helden (Université Libre de Bruxelles, Belgium)
  • Kristel Van Steen (University of Liège, Belgium)
  • Jean-Philippe Vert (Ecole des Mines, France)
  • Louis Wehenkel (University of Liège, Belgium)
  • David Wild (University of Warwick, UK)
  • Jean-Daniel Zucker (University of Paris XIII, France)