Machine Learning in Systems Biology: Submission deadline extended
MLSB 09
Third International Workshop on Machine Learning in Systems Biology
5-6 September 2009, Ljubljana, Slovenia
http://mlsb09.ijs.si/
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.
OBJECTIVE
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.
LOCATION AND CO-LOCATION
The workshop will take place 5-6 September 2009 at the Jozef Stefan Institute, Ljubljana, Slovenia. It will immediately precede ECML PKDD 2009, taking place 7-11 September 2009 in Bled, Slovenia (Bled is 30 miles from Ljubljana, transport will be organized).
SUBMISSIONS INSTRUCTIONS
For an oral presentation, please submit an extended abstract of maximum eight pages. Formatting instructions are available on the website of the workshop. Extended abstracts should be submitted online by 1 June 2009 via the Easychair submission system at
http://www.easychair.org/conferences/?conf=mlsb09.
The accepted submissions will be collected in the proceedings of the workshop.
KEY DATES
12 June: deadline for submission of extended abstracts for oral presentation
10 July: notification for oral presentations
03 August: deadline for submission of abstracts for poster presentations
10 August: notification for posters & camera ready versions due
5-6 September: workshop
TOPICS
A non-exhaustive list of topics suitable for this workshop is given below:
Methods
Machine learning algorithms
Bayesian methods
Data integration/fusion
Feature/subspace selection
Clustering
Biclustering/association rules
Kernel methods
Probabilistic inference
Structured output prediction
Systems identification
Graph inference, completion, smoothing
Semi-supervised learning
Applications
Sequence annotation
Gene expression and post-transcriptional regulation
Inference of gene regulation networks
Gene prediction and whole genome association studies
Metabolic pathway modeling
Signaling networks
Systems biology approaches to biomarker identification
Rational drug design methods
Metabolic reconstruction
Protein function and structure prediction
Protein-protein interaction networks
Synthetic biology
CONFIRMED INVITED SPEAKERS
Ross D. King, Aberystwyth University, UK
William Stafford Noble, University of Washington, USA
MLSB09 PROGRAM CHAIRS
Sašo Džeroski, Jozef Stefan Institute, Ljubljana, Slovenia
Pierre Geurts, Department of EE and CS & GIGA-Research, University of Liège, Belgium
Juho Rousu, Department of Computer Science, University of Helsinki, Finland
SCIENTIFIC PROGRAM COMMITTEE
Florence d’Alché-Buc, University of Evry, France
Saso Dzeroski, Jozef Stefan Institute, Slovenia
Paolo Frasconi, Università degli Studi di Firenze, Italy
Cesare Furlanello, Fondazione Bruno Kessler, Trento, Italy
Pierre Geurts, University of Liège, Belgium
Mark Girolami, University of Glasgow, UK
Dirk Husmeier, Biomathematics & Statistics Scotland, UK
Samuel Kaski, Helsinki University of Technology, Finland
Ross D. King, Aberystwyth University, UK
Neil Lawrence, University of Manchester, UK
Elena Marchiori, Vrije Universiteit Amsterdam, The Netherlands
Yves Moreau, Katholieke Universiteit Leuven, Belgium
William Stafford Noble, University of Washington, USA
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
Guido Sanguinetti, University of Sheffield, UK
Ljupco Todorovski, University of Ljubljana, Slovenia
Koji Tsuda, Max Planck Institute, Tuebingen
Jean-Philippe Vert, Ecole des Mines, France
Louis Wehenkel, University of Liège, Belgium
Jean-Daniel Zucker, University of Paris XIII, France
Blaz Zupan, University of Ljubljana, Slovenia
LOCAL ORGANIZATION
Ivica Slavkov, Dragi Kocev, Tina Anžič, Jozef Stefan Institute,
Ljubljana, Slovenia
