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[bisc-group] [mlnet] Postdoc position: Knowledge Discovery for biomarker identification

From: Amedeo Napoli <amedeo.napoli@loria.fr>
Date: Tue, 24 Jun 2014 18:06:27 +0200 (CEST)
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Cc: Amedeo Napoli <amedeo.napoli@loria.fr>, Estelle Pujos Guillot <estelle.pujos@clermont.inra.fr>
Message-ID: <1870489271.5126969.1403625987691.JavaMail.zimbra@loria.fr>
Berkeley Initiative in Soft Computing (BISC)


Postdoc position available at INRA Clermont-Ferrand -- LORIA/Inria Nancy Grand Est 

Knowledge Discovery for biomarker identification 
(Knowledge Discovery based on Formal Concept Analysis, pattern mining and preferences, for the identification of early predictive biomarkers of diseases) 

Location: Clermont-Ferrand - Nancy 
Duration: 2 years 
Keywords: biomarker, prediction, Formal Concept Analysis, knowledge discovery, multi-dimensional modeling. 

Description of the task. 

The goal of the project is to identify predictive (bio)markers of the evolution of health status toward metabolic syndrome development (from metabolomics signatures, socio-economic parameters and ``food habits''), with the objective of building a model and determining whether the integration of multidimensional parameters improves prediction. Finally, this approach should allow to identify determinants of the evolution of health status. In this project, the volume of data is very important and data are as well heterogeneous (both numerical and symbolic). The integration of large volumes of data can be guided by domain knowledge and be supported by a data schema considered as a mediation system (virtual integration needing correspondences between data sources). This global schema can be based on a concept lattice and defined for materializing the characteristics and the correspondences between data sources. 
The concept lattice provides a classification structure that can be used for various tasks, such as data indexing, information retrieval, data mining, data modeling, and reasoning. The concept lattice is built thanks to Formal Concept Analysis (FCA), which can be considered as a symbolic method for knowledge discovery (KD). It is also planned to use pattern mining methods for extracting frequent or rare patterns and association rules as well. 

In this context, the post-doc fellow’s research will consist in studying the set of data to be analyzed from a theoretical and practical point of view. The theoretical point of view consists in checking which symbolic KD methods are appropriate for analyzing the data and which kind of coupling with numerical KD methods could bring more useful results. 
The practical point of view consists in applying the given methods to the data to be analyzed and to interpret the results. 
Algorithms for FCA, pattern mining and numerical KD methods will be reused but new developments or adaptations are planned for carrying out this project. 

The candidate should prepare a detailed CV including a complete bibliography, a motivation letter and recommendation letters as a single pdf file. This file should be sent by email to both contacts below. 


Estelle Pujos-Guillot, INRA (Institut National de la Recherche Agronomique) 
UMR 1019 Human Nutrition Unit 
Research Centre of Clermont-Ferrand/Theix 
F-63122 St Genès Champanelle France 
Tel: +33 473 624 141 
Email: estelle.pujos@clermont.inra.fr 

Amedeo Napoli, LORIA (CNRS - Inria Nancy Grand Est - Université de Lorraine) 
Équipe Orpailleur - Bâtiment B 
BP 239, F-54506 Vandoeuvre-les-Nancy 
Tel: +33 383 592 068 
Email: Amedeo.Napoli@loria.fr 


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Received on Wednesday, 2 July 2014 12:27:32 UTC

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