[hpc-announce] CfP: CIBB 2016 - Special Session on High-Performance Computing and Deep learning methods for Genomic Data Analysis

Marco Aldinucci aldinuc at di.unito.it
Tue May 10 07:25:16 CDT 2016

Apologies if you receive multiple copies!

                          CALL FOR PAPERS

	  High-Performance Computing and Deep learning methods
		      for Genomic Data Analysis

            13th International Conference on Computational
       Intelligence methods for Bioinformatics and Biostatistics

		  Stirling, UK -- September 1-3, 2016

	  http://www.cs.stir.ac.uk/events/cibb2016/index.html <http://www.cs.stir.ac.uk/events/cibb2016/index.html>


The unprecedented wealth of heterogeneous genomic data has generated an enormous demand for tools and methods to analyse and decipher the complexity of such large information. Genomics bursts on the scene with the most growing data, so much that the Genomic research community is now facing many of the scale-out issues that High-Performance Computing has been addressing for years: it requires powerful infrastructures with fast computing and storage capabilities, with substantial challenges regarding data processing, statistical analysis, and data representation. Traditional techniques and tools for data analytic and autonomous learning are no longer suitable — or even unusable — to extract human-interpretable knowledge and information from this significant amount of data. The aim of this special session is to present the latest advancements concerning High-Performance Computing solutions, deep learning and optimization algorithms required to manage the large-scale challenges outlined above — including related BigData aspects — and to foster the integration of researchers interested in
HPC and Computational Biology.

Examples of topics of interest include, but are not limited to:

   * HPC applications for Bioinformatics
   * HPC architectures for Computational Biology
   * Parallel Machine Learning and Deep Learning approaches for
   * Bioinformatics big data applications and MapReduce implementations
   * Next-Generation Sequencing data analysis and interpretation
   * Differential gene expression analysis and clustering techniques
   * Algorithms for genomic and proteomic
   * Genomic data visualisation

High quality original submissions are solicited for presentation at the Special Session. Papers must be between 4 and 6 pages length, written in latex and submitted in PDF format using the provided latex template and instructions available at:

   http://www.cs.stir.ac.uk/events/cibb2016/cibb2016-sample-v.1.3.zip <http://www.cs.stir.ac.uk/events/cibb2016/cibb2016-sample-v.1.3.zip>.

Papers should be submitted on the EasyChair conference system at the following link:

   https://easychair.org/conferences/?conf=cibb2016 <https://easychair.org/conferences/?conf=cibb2016>

Accepted papers will be presented at the conference and will be published in proceedings for conference distribution. Authors (at least one) of accepted papers are expected to register and present their papers at the conference.

Extended and revised versions of the papers presented at CIBB 2016 will be invited for a post-conference monograph. This is traditionally published in the Springer series of Lecture Notes in Bioinformatics (LNBI) , (arrangements undergoing). Continuing the tradition of CIBB, we are planning to publish the best papers in one (or more, as appropriate) special issue of an international scientific journal (such as BMC Bioinformatics, in the latest editions).

Paper submission deadline: 29th May 2016
Acceptance notification: 22nd June 2016
Camera ready due: 8th July 2016
Authors registration due: 8th July 2016

Zakaria Benmounah - Constantine 2 University, Algeria AND University of Cambridge, UK
Filippo Spiga - University of Cambridge, UK
Fabio Tordini - University of Torino, Italy

CONTACTS: cibb2016 at gmail.com <mailto:cibb2016 at gmail.com>

Marco Aldinucci
Computer Science Dept - University of  Torino - Italy
Homepage: http://di.unito.it/aldinuc <http://di.unito.it/aldinuc> 
FastFlow: http://di.unito.it/fastflow <http://di.unito.it/fastflow>

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