Connectionists: ESANN'18: 2nd CFP: Sp.Sess. on DEEP LEARNING in BIOINFORMATICS and MEDICINE

[Alfredo Vellido]

***Apologies for crossposting***

2nd CFP:  special session on "DEEP LEARNING in BIOINFORMATICS and 
MEDICINE" at ESANN 2018

European Symposium on Artificial Neural Networks, Computational 
Intelligence and Machine Learning.
25-27 April 2018, Bruges, Belgium
www.esann.org

DESCRIPTION:
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).
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.
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.
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.
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.
Topics include, but are not restricted to:
-  DL applications and novel models for biology, chemistry, genetics, 
medicine and omics-data
-  Interpretability and provable properties of DL models.
-  Learning representations from multi-modal bioinformatics data.
-  Deep models for visual analytics and inspection of biomedical data.
-  NLP for knowledge discovery in the medicine field.
-  Deep Reinforcement Learning for the optimization of medical treatments.
-  DL for structured data processing in bioinformatics and chemistry.
-  High performance computing for DL and bioinformatics.
-  Software frameworks and toolkits specific for DL in bioinformatics 
and medical applications.

SUBMISSION:
Through ESANN web: http://www.elen.ucl.ac.be/esann/index.php?pg=submission.

PRELIMINARY DATES:
Paper submission deadline : 20 November 2017
Notification of acceptance : 31 January 2018

SPECIAL SESSION ORGANISERS:
- Miguel Atencia, Universidad de Málaga (Spain) matencia at ctima.uma.es / 
http://www.matap.uma.es/profesor/matencia
- Davide Bacciu, Università di Pisa (Italy) bacciu at di.unipi.it  / 
http://pages.di.unipi.it/bacciu
- Paulo J.G. Lisboa, Liverpool John Moores University (U.K.) 
P.J.Lisboa at ljmu.ac.uk  / 
https://www.ljmu.ac.uk/about-us/staff-profiles/faculty-of-engineering-and-technology/department-of-applied-mathematics/paulo-lisboa 

- José D. Martin, Universitat de València (Spain) jose.d.martin at uv.es  
/  http://www.uv.es/jdmg
- Ruxandra Stoean, University of Craiova (Romania) rstoean at inf.ucv.ro  
/  http://inf.ucv.ro/~rstoean
- Alfredo Vellido, Universitat Politècnica de Catalunya (Spain) 
avellido at lsi.upc.edu  /  www.cs.upc.edu/~avellido