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WABI 2013 : 13th Workshop on Algorithms in Bioinformatics

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Conference Series : Workshop on Algorithms in Bioinformatics
 
Link: http://algo2013.inria.fr/wabi.shtml
 
When Sep 2, 2013 - Sep 4, 2013
Where Nice, France, EU
Submission Deadline May 17, 2013
Notification Due Jun 14, 2013
Final Version Due Jun 24, 2013
Categories    bioinformatics   algorithms   computational biology   machine learning
 

Call For Papers

Workshop on Algorithms in Bioinformatics 2013

The Workshop on Algorithms in Bioinformatics (WABI), established in 2001, is one of the major annual conferences on algorithms in bioinformatics. WABI 2013 will be part of ALGO 2013, hosted by INRIA and Campus SophiaTech, in Sophia Antipolis, France. WABI is sponsored by the European Association for Theoretical Computer Science (EATCS), and the International Society for Computational Biology (ISCB).

Scope

All research in algorithmic work in bioinformatics, computational biology and systems biology. The emphasis is mainly on discrete algorithms and machine-learning methods that address important problems in molecular biology, that are founded on sound models, that are computationally efficient, and that have been implemented and tested in simulations and on real datasets. The goal is to present recent research results, including significant work-in-progress, and to identify and explore directions of future research.

Topics

Original research papers (including significant work-in-progress) or state-of-the-art surveys are solicited in all aspects of algorithms in bioinformatics, computational biology and systems biology - including, but not limited to:

Exact and approximate algorithms for sequence analysis, gene and signal recognition, alignment and assembly, molecular evolution, structure determination or prediction, gene expression, molecular pathways and network, proteomics, functional and comparative genomics, and drug design.
Methods, software, and dataset repositories for development and testing of such algorithms and their underlying models, as well as high-performance computing approaches to computationally hard learning and optimization problems.
Novel approaches to analyzing and modeling next-generation sequence data, including sequence assembly, population genomics, metagenomics, metatranscriptomics and ncRNA sequencing.

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