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May 2008

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ECML/PKDD workshop on Statistical and Relational learning in Bioinformatics 1st CFP
by Jan Ramon 23 May '08

23 May '08

ECML/PKDD Workshop on Statistical and Relational learning in Bioinformatics Friday, September 19th, 2008 Antwerp, Belgium http://www.ecmlpkdd2008.org/StReBio MOTIVATION There is an increasing interest for structured data in the machine learning community as shown by the growing number of dedicated Conferences and Workshops (MLG, SRL, ILP, MRDM). Bioinformatics is an application domain of increasing popularity where information is naturally represented in terms of relations between (possibly heterogeneous) objects. The Workshop on Relational Learning in Bioinformatics focuses on learning methods for structured biological data (relational data, graphs, logic based descriptions, etc) in the presence of uncertainty (probabilistic logic models, Bayesian methods, etc). These methods are well-suited for this application area, since the available data is highly complex and tends to have a significant amount of missing information. SCOPE The workshop aims at bringing together researchers from both the field of relational learning, machine learning over structured data and biology. We therefore invite submissions that describe new methods, problem settings, applications and models, exploiting structured data in the field of biology. Methods include, but are not restricted to * Statistical Relational Learning * Relational Probabilistic Models * Inductive Logic Programming * Multi-relational Data Mining * Graph Methods The data, structures or models considered can include but are not limited to * Sequences (DNA, RNA, protein) * Pathways (chemical, metabolic, mutation, interaction pathways) * 2D, 3D structures of proteins, RNA * Chemical structures (e.g. QSAR, especially regarding interaction of compounds with proteins) * Evolutionary relations (phylogeny, homology relations) * Ontologies integration (gene, enzyme, protein function ontologies) * Large networks (regulatory, co-expression, interaction, and metabolic networks) * Concept graphs (including compounds, articles, authors, references) SUBMISSIONS We invite both * regular papers describing contributions to the field and * problem statements, explaining relevant but not yet adequately solved problems, in terms which are clear for the computer scientist. Important Dates: * Paper deadline: June 16 * Notifications of Acceptance: July 16 * Final versions: August 14 More information can be found at the workshop website http://www.ecmlpkdd2008.org/StReBio

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2nd CFP - Machine Learning in Systems Biology - 13-14 Sept, Brussels, Belgium
by Pierre Geurts 22 May '08

22 May '08

[apologies for multiple postings] ********************** Second Call for Papers ************************** 2nd International Workshop on Machine Learning in Systems Biology 13-14 September 2008, Brussels, Belgium ************************************************************************ http://www.montefiore.ulg.ac.be/services/stochastic/mlsb08 MOTIVATION 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 theirname. 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. OBJECTIVE 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. 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: 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 Structure Prediction Protein Function Prediction Protein-protein interaction networks SUBMISSIONS OF EXTENDED ABSTRACTS We invite to submit an extended abstract of maximum 8 pages. Each extended abstract must be submitted by June 15 2008 electronically via the Easychair submission system: http://www.easychair.org/conferences/?conf=mlsb08 Formatting instructions can be found on the workshop website http://www.montefiore.ulg.ac.be/services/stochastic/mlsb08 The extended abstracts will be reviewed by the scientific programme committee. The papers will be selected for oral or poster presentation according to their originality and relevance to the workshop topics. Electronic versions of the extended abstracts will be accessible to the participants prior to the conference and will be made publicly available on the conference web site after the conference. It is also planned to publish these papers in a special volume of a bioinformatics journal. SUBMISSION OF POSTER PRESENTATIONS We invite you also to apply for poster presentations on topics of relevance to the workshop. Each poster presentation should be described in a 1 page summary and be submitted by August 20, 2008 via the Easychair submission system: http://www.easychair.org/conferences/?conf=mlsb08 Poster summaries must follow the same formatting instructions as the extended abstract, but should not exceed one page. IMPORTANT DATES - 15 June: deadline for submissions of extended abstracts - 15 July: notification of acceptance to authors - 25 August: camera-ready versions of extended abstracts - 20 August: deadline for submissions of poster presentations - 25 August: notification of acceptance to authors - 13-14 September: workshop LOCATION The workshop will take place in Brussels at the Palais des Académies of Académie royale des Sciences, des Lettres et des Beaux-Arts de Belgique. CONTACT For further information, please contact mlsb08(a)gmail.com or see the conference website: http://www.montefiore.ulg.ac.be/services/stochastic/mlsb08 MLSB08 CHAIRS Louis Wehenkel and Pierre Geurts, GIGA-Research, University of Liège, Belgium Florence d’Alché-Buc, IBISC CNRS FRE 2873, University of Evry-Val d’Essonne, France Yves Moreau, ESAT, Katholieke Universiteit Leuven, Belgium SCIENTIFIC PROGRAMME 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)

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