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<p>***Apologies for crossposting***<br>
<br>
<b>1st CFP: special session on "DEEP LEARNING in BIOINFORMATICS
and MEDICINE" at ESANN 2018</b><b><br>
</b><b><br>
</b>European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning. <br>
25-27 April 2018, Bruges, Belgium <br>
<a class="moz-txt-link-abbreviated" href="http://www.esann.org">www.esann.org</a><br>
<br>
<b>DESCRIPTION:</b><b><br>
</b>Deep learning (DL) has been harnessing the attention of the
machine learning research community over the latter years. Much of
its success roots on having made available models and technologies
capable of achieving ground-breaking performances in a variety of
traditional fields of application of machine learning, such as
machine vision and natural language processing (NLP).<br>
Medicine, genetics, biology and chemistry are among the research
fields where machine learning models find most consolidated
applications. Admittedly, some of the DL flagships, like NLP and
image processing have their implications in Medicine, e.g., in
extracting information from the text of patients’ records or in
analyzing medical imagery to find anomalous patterns. <br>
However, DL methodologies have only recently started to be used to
address relevant bioinformatics and cheminformatics challenges.
Reasons for such a slowed-down permeation can be sought in the
complexity of the DL models which might prove difficult to use in
novel application fields by non-machine learning experts. Lack of
interpretability and insight into the trained models might also
have been a limiting factor.<br>
Despite such few limitations, DL methodologies offer far more
enabling aspects and technologies for developing impacting
contributions in bioinformatics research. Between the most
relevant are the ability to effectively and efficiently process
complex, large scale and multi-modal data, e.g. collections of
biomedical images and associated patient information, DNA
sequences, molecular graphs. The modular design of deep
architectures together with the potential for re-using parts of
previously trained models on novel tasks is another potential
success enabler for bioinformatics applications.<br>
This special session is meant to attract researchers who develop,
investigate, or apply DL methods on biomedical and chemistry data.
We aim to bring together researchers working on the topic from
both the deep learning and the bioinformatics communities.<br>
Topics include, but are not restricted to:<br>
- DL applications and novel models for biology, chemistry,
genetics, medicine and omics-data<br>
- Interpretability and provable properties of DL models.<br>
- Learning representations from multi-modal bioinformatics data.<br>
- Deep models for visual analytics and inspection of biomedical
data.<br>
- NLP for knowledge discovery in the medicine field.<br>
- Deep Reinforcement Learning for the optimization of medical
treatments.<br>
- DL for structured data processing in bioinformatics and
chemistry.<br>
- High performance computing for DL and bioinformatics.<br>
- Software frameworks and toolkits specific for DL in
bioinformatics and medical applications.<br>
<br>
<b>SUBMISSION:</b><b><br>
</b>Through ESANN web:
<a class="moz-txt-link-freetext" href="http://www.elen.ucl.ac.be/esann/index.php?pg=submission">http://www.elen.ucl.ac.be/esann/index.php?pg=submission</a>.<br>
<br>
<b>PRELIMINARY DATES:</b><b><br>
</b>Paper submission deadline : 20 November 2017<br>
Notification of acceptance : 31 January 2018<br>
<br>
<b>SPECIAL SESSION ORGANISERS:</b><b><br>
</b>- Miguel Atencia, Universidad de Málaga (Spain)
<a class="moz-txt-link-abbreviated" href="mailto:matencia@ctima.uma.es">matencia@ctima.uma.es</a> / <a class="moz-txt-link-freetext" href="http://www.matap.uma.es/profesor/matencia">http://www.matap.uma.es/profesor/matencia</a><br>
- Davide Bacciu, Università di Pisa (Italy) <a class="moz-txt-link-abbreviated" href="mailto:bacciu@di.unipi.it">bacciu@di.unipi.it</a> /
<a class="moz-txt-link-freetext" href="http://pages.di.unipi.it/bacciu">http://pages.di.unipi.it/bacciu</a><br>
- Paulo J.G. Lisboa, Liverpool John Moores University (U.K.)
<a class="moz-txt-link-abbreviated" href="mailto:P.J.Lisboa@ljmu.ac.uk">P.J.Lisboa@ljmu.ac.uk</a> /
<a class="moz-txt-link-freetext" href="https://www.ljmu.ac.uk/about-us/staff-profiles/faculty-of-engineering-and-technology/department-of-applied-mathematics/paulo-lisboa">https://www.ljmu.ac.uk/about-us/staff-profiles/faculty-of-engineering-and-technology/department-of-applied-mathematics/paulo-lisboa</a><br>
- José D. Martin, Universitat de València (Spain)
<a class="moz-txt-link-abbreviated" href="mailto:jose.d.martin@uv.es">jose.d.martin@uv.es</a> / <a class="moz-txt-link-freetext" href="http://www.uv.es/jdmg">http://www.uv.es/jdmg</a><br>
- Ruxandra Stoean, University of Craiova (Romania)
<a class="moz-txt-link-abbreviated" href="mailto:rstoean@inf.ucv.ro">rstoean@inf.ucv.ro</a> / <a class="moz-txt-link-freetext" href="http://inf.ucv.ro/~rstoean">http://inf.ucv.ro/~rstoean</a><br>
- Alfredo Vellido, Universitat Politècnica de Catalunya (Spain)
<a class="moz-txt-link-abbreviated" href="mailto:avellido@lsi.upc.edu">avellido@lsi.upc.edu</a> / <a class="moz-txt-link-freetext" href="http://www.cs.upc.edu/~avellido">http://www.cs.upc.edu/~avellido</a><br>
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