Connectionists: MLSB13, the 7th Machine Learning in Systems Biology workshop

[Jean-Philippe Vert]

                   Call for contributions

     MLSB13, the Seventh International Workshop on 
           Machine Learning in Systems Biology

                    http://www.mlsb.cc


    Organized in conjunction with ISMB/ECCB 2013
               Berlin, Germany, July 19-20, 2013.


Important dates:
May 5, 2013 : Deadline for submission of extended abstracts
May 31, 2013: Author notification
July 19-20, 2013: Workshop

WORKSHOP DESCRIPTION

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.

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. 

SUBMISSION INSTRUCTIONS

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 found on the home page, as well as examples of work presented in previous years.

We invite you to submit an extended abstract of up to 4 pages in PDF format describing new or recently published (2013) results. We also welcome original ISMB submissions of relevance to the workshop, which were accepted into the second round of reviews but unfortunately not selected for the main conference. In this case, there is no need to prepare a separate abstract; it is OK to simply submit your revised paper.

Extended abstracts should be uploaded to the MLSB submission web site:

https://www.easychair.org/conferences/?conf=mlsb13

by May 5,  2013, 11:59pm (time zone of your choice).

No special style is required, as long as standard font size (11 pt) and margins (1 in) are used. Submissions will be reviewed by the scientific programme committee. They 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, distributed in hardcopy form to participants at the conference, and will be made publicly available on the conference web site after the conference. However, the book of abstracts will not be published and the extended abstracts will not constitute a formal publication.

INVITED SPEAKERS

Sayan Mukherjee, Duke University, North Carolina, USA 
Guido Sanguinetti, University of Edinburgh, UK 
Eran Segal, Weizmann Institute, Israel 
Lani Wu, University of Texas Southwestern Medical Center, USA

PROGRAM COMMITTEE

Karsten Borgwardt (Max Planck Institute, Tuebingin)
Florence d'Alché-Buc (University of Evry, France)
Sašo Džeroski (Jožef Stefan Institute, Slovenia)
Paolo Frasconi (University of Florence, Italy)
Pierre Geurts (University of Liège, Belgium)
Lars Kaderali (TU Dresden, Germany)
Samuel Kaski (Aalto University and University of Helsinki, Finland)
Ross King (Manchester University, UK)
Stefan Kramer (University of Mainz, Germany)
Christina Leslie (Memorial Sloan-Kettering Cancer Center, USA)
Yves Moreau (Katholieke Universiteit Leuven, Belgium)
Mahesan Niranjan (University of Southampton, UK)
John Pinney (Imperial College London , UK)
Magnus Rattray (Manchester University, UK)
Simon Rogers (University of Glasgow, UK)
Juho Rousu (University of Helsinki, Finland)
Céline Rouveirol (Paris 13 University, France)
Yvan Saeys (University of Gent, Belgium)
Guido Sanguinetti (University of Edinburgh, UK)
Peter Sykacek (BOKU University, Austria)
Ljupco Todorovski (University of Ljubljana, Slovenia)
Achim Tresch (MPI for Plant Breeding, Cologne)
Koji Tsuda (National Institute of Advanced Industrial Science and Technology, Japan)
Louis Wehenkel University of Liège, Belgium)
Filip Zelezny (Czech Technical University in Prague, Czech Republic)

ORGANIZERS

Uwe Ohler (Berlin Institute for Molecular Systems Biology / Max Delbrueck Center, Germany)
Jean-Philippe Vert (Mines ParisTech, Institut Curie, France)




-- 
Jean-Philippe Vert
Cancer computational genomics and bioinformatics
Mines ParisTech - Institut Curie - INSERM U900
http://cbio.ensmp.fr/~jvert

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