W09 - Workshop on Machine Learning for Systems Biology


  • Deadline for submission has been extented to June 22¬†
  • Abstracts for poster presentations can be submitted until July 13

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 "omics" information has become a condition 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.

MLSB14, the Eighth International Workshop on Machine Learning in Systems Biology, is a workshop of the ECCB 2014 conference. It aims to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology by bringing together method developers and experimentalists. We are soliciting submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis

Please see the workshop website http://www.mlsb.cc for more details.


We invite you to submit an extended abstract of up to 4 pages in PDF format describing new or very recently published results. 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.

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.

Key dates:

  • Submission deadline for extended abstracts for MLSB: June 11, 2014 Extended to June 22, 2014
  • Submission deadline for poster presentation only: July 13, 2014
  • Author notification for extended abstracts for MLSB: July 15, 2014
  • Early registration deadline: August 2, 2014
  • Workshop: September 6-7, 2014


  • Florence d'Alch√©-Buc (University of Evry, France)
  • Pierre Geurts (University of Liege, Belgium)

Organizing committee

  • Florence d'Alch√©-Buc (University of Evry, France)
  • Markus Heinonen (University of Evry, France)
  • Pierre Geurts (University of Liege, Belgium)
  • V√Ęn Anh Huynh-Thu (University of Edinburgh, UK)


For further information, please contact This email address is being protected from spambots. You need JavaScript enabled to view it.

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