MLSB11, the Fifth International Workshop on Machine Learning in Systems Biology will be held in Vienna, Austria on July 20-21, 2011.

The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists. We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis.

Complete workshop proceedings can be downloaded from here .

The Workshop is organized as "Satellite Meeting" of the 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 10th European Conference on Computational Biology (ECCB).

Motivation

Molecular biology and 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, the amount of available experimental data is not a limiting factor any more; on the contrary, there is a plethora of it. Given the research question, the challenge has shifted towards identifying the relevant pieces of information and making sense out of it (a "data mining" issue). Second, rather than focus on components in isolation, we can now try to understand how biological systems behave as a 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 in the biomedical sciences in general. 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.

Molecular biology and 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.

Topics

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 function and structure prediction
Graph inference, completion, smoothing	Protein-protein interaction networks
Semi-supervised learning	Synthetic biology

MLSB10 Chairs

Stefan Kramer	Technische Universität München
Neil Lawrence	University of Sheffield

Scientific Program Committee

Florence d'Alché-Buc (University of Evry, France)
Hendrik Blockeel (Katholieke Universiteit Leuven, Belgium)
Sašo Džeroski (Jožef Stefan Institute, Slovenia)
Paolo Frasconi (Università degli Studi di Firenze, Italy)
Pierre Geurts (University of Liège, Belgium)
Dirk Husmeier (Biomathematics & Statistics Scotland, UK)
Lars Kaderali (University of Heidelberg, Germany)
Samuel Kaski (Helsinki University of Technology, Finland)
Ross King (Aberystwyth University, UK)
Stefan Kramer (TU München, Germany)
Neil Lawrence (University of Sheffield, UK)
Elena Marchiori (Vrije Universiteit Amsterdam, The Netherlands)
Yves Moreau (Katholieke Universiteit Leuven, Belgium)
Sach Mukherjee (University of Warwick, UK)
Mahesan Niranjan (University of Southampton, UK)
John Pinney (Imperial College London , UK)
Gunnar R�tsch (Friedrich Miescher Laboratory of the Max Planck Society, Germany)
Magnus Rattray (University of Manchester, UK)
Simon Rogers (University of Glasgow, UK)
Juho Rousu (University of Helsinki, Finland)
Céline Rouveirol (University of Paris XIII, France)
Yvan Saeys (University of Gent, Belgium)
Guido Sanguinetti (University of Sheffield/University of Edinburgh, UK)
Peter Sykacek (BOKU University, Austria)
Fabian Theis (TU München, Germany)
Ljupco Todorovski (University of Ljubljana, Slovenia)
Koji Tsuda (National Institute of Advanced Industrial Science and Technology, Japan)
Jean-Philippe Vert (Ecole des Mines, France)
Louis Wehenkel (University of Liège, Belgium)
Filip Zelezny (Czech Technical University in Prague, Czech Republic)

Local Organizing Committee

Contact

For further information, please contact mlsb@mailkramer.in.tum.de