| Eighth International Workshop on Machine Learning in Systems Biology (MLSB 2014) | |
|
France - Strasbourg |
|
| Hosted by: | ECCB 2014 |
| Dates: | Sep 06, 2014 through Sep 07, 2014 |
| Call for Proceedings Presentations: | 2014-05-01 through 2014-06-11 |
| Event Registration: | 2014-05-01 through 2014-01-01 |
| Early Registration Deadline: | 2014-08-02 |
|
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 "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. SUBMISSIONS INSTRUCTIONS 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. KEY DATES Submission deadline: June 11, 2014 Author notification: July 15, 2014 Early registration deadline ECCB14: August 2, 2014 Workshop: September 6-7, 2014 CHAIRS Florence d’Alché-Buc (University of Evry, France) Pierre Geurts (University of Liege, Belgium) ORGANIZING COMMITTEE Florence d’Alché-Buc (University of Evry, France) Markus Heinonen (University of Evry, France) Pierre Geurts (University of Liege, Belgium) Vân Anh Huynh-Thu (University of Edinburgh, UK) Nizar Touleimat (Centre National de génotypage, CEA, Evry, France) CONTACT For further information, please contact chairsmlsb2014@gmail.com |
|
| Additional Information | |
| Event URL: | http://www.mlsb.cc/ |
| ISCB Member Discount: | None |
| Contact Person: | Florence d’Alché-Buc, Pierre Geurts ([javascript protected email address]) |
While ISCB provides for conference and event listings that may be of interest to members and bioinformaticians at large, ISCB is not responsible for the content provided by outside sources. Such listings are not meant as an endorsement by ISCB.
