MLSB 2008



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.


The list of accepted papers for oral presentations and invited speakers is now complete. A provisional schedule is available here.


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.


We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis. A non-exhaustive list of topics suitable for this workshop are:

Methods Applications
Machine Learning Algorithms Sequence Annotation
Bayesian Methods Gene Expression and post-transcriptional regulation
Data integration/fusion Inference of gene regulation networks
Feature/subspace selection Gene prediction and whole genome association studies
Clustering Metabolic pathway modeling
Biclustering/association rules Signaling networks
Kernel Methods Systems biology approaches to biomarker identification
Probabilistic inference Rational drug design methods
Structured output prediction Metabolic reconstruction
Systems identification Protein structure prediction
Graph inference, completion, smoothing Protein function prediction
Semi-supervised learning Protein-protein interaction networks


MLSB08 Chairs

Louis Wehenkel and Pierre Geurts GIGA-Research, University of Liège, Belgium
Yves Moreau ESAT, K U Leuven, Belgium
Florence d'Alché-Buc IBISC CNRS FRE 2873, University of Evry, France


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)



For further information, please contact


Related events

MLSB 08 is organized just before the ECML/PKDD conference in Antwerp, which is only half an hour from Brussels. After this conference, there is also a more specific workshop on Statistical and relational learning in bioinformatics.



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