[BBC] [bioinfo] CFP MLSB17 11th Machine Learning in Systems Biology Workshop

[Alexander Botzki]

From: Chloe-Agathe Azencott [mailto:chloe-agathe.azencott at mines-paristech.fr] 
Sent: 17 March 2017 11:12
To: bioinfo at sfbi.fr
Subject: [bioinfo] CFP MLSB17 11th Machine Learning in Systems Biology Workshop

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                    CALL FOR ABSTRACTS -- MLSB 2017

The 11th International Workshop on Machine Learning in Systems Biology
                           http://www.mlsb.cc

            Organized as a Special Session at ISMB/ECCB 2017
                              July 25, 2017
                         Prague, Czech Republic

               *Abstract submission deadline: May 18, 2017*
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Biology is rapidly turning into an information science, thanks to enormous advances in the ability to observe the molecular properties of cells, organs and individuals. This wealth of data allows us to model molecular systems at an unprecedented level of detail and to start to understand the underlying biological mechanisms. This field of systems biology creates a huge need for methods from machine learning, which find statistical dependencies and patterns in these large-scale datasets and that use them to establish models of complex molecular systems. MLSB is a scientific forum for the exchange between researchers from Systems Biology and Machine Learning, to promote the exchange of ideas, interactions and collaborations between these communities.

The aim of MLSB 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.

KEY DATES
      Submission deadline: May 18, 2017
      Author notification: June 8, 2017
      Early bird registration deadline ISMB/ECCB 2017: June 15, 2017
      MLSB 2017: July 25, 2017

SUBMISSIONS

We encourage submissions presenting novel methods for discovering complex structures (e.g. interaction networks, molecular structures), statistical machine learning methods for analysis of various high-throughput -omics data, and methods supporting systems-level data analysis.

We are calling for 1-4 page abstracts describing either:
     - Original work on machine learning in systems biology. These should describe method development and application, and we encourage authors to submit recent ideas for discussion.
     - Highlights describing computational aspects of work that has recently been published or accepted for publication in a peer-reviewed journal or at the main conference.

Abstracts will be considered for a talk or a poster presentation. The abstract must be in PDF format (file extension .pdf) and can be up to 4 pages long, including all figures and references. If you only wish to be considered for a poster, then a one page abstract is sufficient.

A non-exhaustive list of topics suitable for this workshop are:
     - Active learning/experimental design
     - Bayesian methods
     - Biomarker identification
     - Clustering/biclustering
     - Data integration/fusion/multi-view learning
     - Deep learning
     - Epigenetics
     - Feature/subspace selection
     - Genome-wide association studies
     - Graph inference/completion
     - Kernel methods
     - Machine learning algorithms
     - Metabolic modeling and reconstruction
     - Metabolomics
     - Multitask/structured output prediction
     - Precision medicine
     - Probabilistic inference
     - Protein function and structure prediction
     - Protein-protein interaction networks
     - Rational drug design
     - Regulatory genomics
     - Semi-supervised learning
     - Sequence annotation
     - Signaling networks
     - Synthetic biology
     - Systems identification
     - Time-series analysis
     - Transcriptomics

CHAIRS
     Chloé-Agathe Azencott (MINES ParisTech, France)
     Magnus Rattray (University of Manchester, UK)

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Chloe-Agathe Azencott
CBIO Mines ParisTech - Institut Curie - INSERM U900 http://cazencott.info