Connectionists: ESANN'08 Special Session CFP: Machine Learning Methods in Cancer Research

[Lisboa, Paulo]

*** CALL FOR PAPERS *** Apologies for crossposting
 
SPECIAL SESSION: Machine learning methods in cancer research
Organizers: Alfredo Vellido (Tech. Univ. Catalunya, Spain), Paulo J.G. 
Lisboa (Liverpool John Moores University, U.K.)
 
*** As part of ESANN'2008 ***
16th European Symposium on Artificial Neural Networks
Advances in Computational Intelligence and Learning
Bruges (Belgium) - April 23-24-25, 2008
 
THE SESSION IN BRIEF:
Neural Networks and Machine Learning methods in general are widely used
in cancer research and published in clinical, as well as methodological
journals. Their acceptance among medical practitioners is steadily
increasing, in part because of demands for advanced data analysis
relating to bioinformatics, but also because of a realization that
decision support will be inherent in the current agenda for personalized
medicine. The application of Machine Learning to medical data may be
said to have entered a period of adolescence, where the early excitement
about their potential has been tempered by the need to assure generality
through the use of principled approaches to complexity control. The
excitement that was communicated during the early phase of development
in the late 90's seems to have whetted the appetite of clinicians for
what these methods can achieve, initiating close and fruitful
collaborations where key clinical questions are driving new data-based
studies, so building clinical relevance, rather than obsolescence, into
study design.
Machine Learning methods can be applied to a wide range of data types
and problems in cancer research. The range of applications includes
exploratory analysis and predictive inference, with topics ranging from
clustering, through classification, survival analysis, and rule
extraction. Hot topics include knowledge discovery from data, but also
the integration of multimodal data into clinical inference systems, the
use of graphical models for structure finding in large sparse data sets,
and methods for robust performance estimation which include the use of
automatic rule extraction methods to match inference making with
clinical expert knowledge. This special session aims to bring together
methodological advances and clinical relevant case studies of Machine
Learning approaches to cancer diagnosis and prognosis, and
oncology-related bioinformatics. ESANN 2008 participants would benefit 
from the coming together of a number of internationally renowned experts
in the field, who would provide their expert view on a broad palette of
state-of-the-art theoretical developments and applications.
 
Further information on the web: 
www.dice.ucl.ac.be/esann/index.php?pg=specsess#Machine%20learning%20meth
ods%20in%20cancer%20research
www.lsi.upc.edu/~avellido/research/ESANN08-SpecialSessionCFP.html
 
***** Deadline for submission of papers: November 23, 2007 ***** The
electronic submission procedure is described on the ESANN portal 
http://www.dice.ucl.ac.be/esann/
 
 
CONTACT
Alfredo Vellido, PhD
Department of Computing Languages and Systems. Polytechnic University of

Catalonia
Barcelona, Spain
Tel.: +34 93 4137796
Fax: +34 93 4137833
email: avellido at lsi.upc.edu
 
Paulo J.G. Lisboa, PhD
School of Computing and Mathematical Sciences. Liverpool John Moores 
University
Liverpool, United Kingdom
Tel.: +44 151 2312225
Fax: +44 151 2074594
email: P.J.Lisboa at ljmu.ac.uk
 
 
ESANN'2008 is organized in collaboration with the UCL (Universite
catholique de Louvain, Louvain-la-Neuve) and the KULeuven (Katholiek
Universiteit Leuven). The conference is technically co-sponsored by the
International Neural Networks Society, the European Neural Networks
Society, the IEEE Computational Intelligence Society, the IEEE Region 8,
the IEEE Benelux Section (sponsors to be confirmed).
 
 
Proceedings and journal special issue
-------------------------------------
The proceedings will include all communications presented to the
conference (tutorials, oral and posters), and will be available on-site.
Extended versions of selected papers will be published in the
Neurocomputing journal (Elsevier).