SDC, Data Science and Knowledge

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PhD student in the [[Home|SDC]] team (formerly BFO team) of the [http://icube.unistra.fr ICube] laboratory of the [http://unistra.fr/index.php?id=homepage University of Strasbourg] since October 2012.
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PhD student in the [[Home|SDC]] team (formerly BFO team) of the [http://icube.unistra.fr ICube] laboratory of the [http://unistra.fr/index.php?id=homepage University of Strasbourg] since May 2015.
   
 
= Contact =
 
= Contact =
   
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Ali AYADI
Clément CHARNAY
 
 
ICube Laboratory
 
ICube Laboratory
 
Télécom Physique Strasbourg
 
Télécom Physique Strasbourg
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F - 67412 Illkirch cedex
 
F - 67412 Illkirch cedex
   
Office: C331
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Office: C335
Phone: +33 (0) 3 68 85 45 78
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Phone: +33 (0) 6 56 76 34 46
Email: charnay (at) unistra (dot) fr
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Email: ali.ayadi (at) unistra (dot) fr
   
 
= Research =
 
= Research =
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== PhD Thesis ==
 
== PhD Thesis ==
   
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'''Title: ''' Semantic technologies for the optimization of complex biomolecular networks
'''Title: ''' Quantification in Relational Data Mining
 
   
'''Promotor: ''' [[Nicolas_Lachiche|Nicolas Lachiche]] (Tenured Senior Associate Professor, ICube-SDC)
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'''Promotor: ''' [http://icube-sdc.unistra.fr/en/index.php/Cecilia_Zanni-Merk Cecilia Zanni-Merk] (Senior Tenured Associate Professor, ICube-SDC and INSA Strasbourg)
   
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'''Co-advisor: ''' [http://icube-sdc.unistra.fr/en/index.php?title=Fran%C3%A7ois_de_Bertrand_de_Beuvron&action=edit&redlink=1 François de Bertrand de Beuvron] (Tenured Associate Professor, ICube-SDC and INSA Strasbourg) and [http://icube-bfo.unistra.fr/en/index.php/Julie_Thompson Julie Thompson] (Research director CNRS, at [http://icube-cstb.unistra.fr/fr/index.php/Accueil ICube-CSTB])
'''Co-advisor: ''' [[Agnès_Braud|Agnès Braud]] (Tenured Associate Professor, ICube-SDC)
 
   
'''Funding: ''' Grant from the French Ministry of Higher Education and Research
 
   
'''Overview: ''' This PhD thesis focuses on two strong points of the Data Mining Theme of the BFO team of ICube: relational data mining on the one hand, and cost-sensitive learning on the other hand. These two points are currently studied as part of the european project [http://www.reframe-d2k.org/index.php/Main_Page REFRAME], in collaboration with the [http://www.bris.ac.uk University of Bristol] and the [http://www.upv.es/index-en.html Polytechnic University of Valencia].
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'''Overview: ''' This PhD thesis is prepared as part of a Franco-Tunisian cotutelle between, the [http://icube-sdc.unistra.fr/en/index.php/Home SDC Team] in collaboration with the [http://lbgi.fr/lbgi/ LBGI Team] and the [http://www.utunis.rnu.tn/ University of Tunis].
  +
The general goal of this thesis is to find an optimal set of external stimuli to be applied during a predetermined time interval to evolve the network from its current state to another
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desired state. Our original approach is based on the combined use of semantic technologies, combinatorial optimization and simulation.
   
  +
A complex biomolecular network is represented by the interactions of many molecules (genes, proteins and metabolites) in a cell. This network should stay at a normal (at least, healthy) phenotype. However, by some unknown perturbation or stimuli, the network can be transited from a normal phenotype to a disease phenotype. It thus is desirable to steer the biomolecular network to transit from the abnormal phenotype to a healthy phenotype.
Relational data mining is a subfield of data mining where data is not represented according to the classic attribute-value model, in which every row of a single table would represent a training instance of a model with its properties, including the attribute to predict. Here, data is represented by several tables linked with foreign keys, which represent the different kinds of objects constituting the problem. A table, called the main table, contains the training instances (for instance, molecules) with the attribute to learn and other tables (for instance a table of the atoms constituting the molecules) contain the secondary objects linked to the main ones. We intend to take into account the properties of such secondary objects in the learning process on the main objects. A way to do so, in which we are more particularly interested, is the use of complex aggregates. They constitute a way to aggregate the secondary objects linked to one main object that meet a certain condition. More intuitively, the allow to summarize in one value the secondary table. Two examples of such an aggregate would be the number of carbon atoms in the molecule, or the average charge of the oxygen atoms of the molecule. However, the number of possibilities for the aggregate condition and the aggregate function make the exhaustive generation of all complex aggregates intractable. One of the goals of the PhD thesis is to propose a heuristic allowing to explore the complex aggregate space and to generate incrementally the ones that are relevant to address the given problem.
 
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Here we are interested in how to effectively steer the system from an unexpected state to a desired state by applying suitable input control signals. The main purpose of this project is to provide a platform based on two strong points of the Data Mining Theme of the SDC team of ICube: semantic technologies on the one hand, and combinatorial optimisation tools on the other hand.
   
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With this aim in mind, our future work will continue to develop a platform to study the transitions of biomolecular networks from any state to a specific state, based on three modules: (i) The ontological module: This module uses semantic technologies to generate new inferred knowledges (the discovery of new semantic associations between molecules) to
The other domain on which this PhD thesis focuses on is multi-class cost-sensitive learning. In this kind of problem, the attribute to learn can take many values, ''i.e.'' more than 2, contrary to the binary problems for which many learning algorithms are designed. Moreover, all the classification errors do not have the same cost, as expected in a medical domain, where diagnosing a disease for a sane patient will not have the same impact as not diagnosing the disease for a sick patient. In this framework, we are particularly interested in to binarization approaches, which consist in reducing a multi-class problem into several binary problems. More particularly, we consider the case where the binarization uses scorers, the scores being used to set decision thresholds between the two classes of the binary subproblems.
 
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refine the transitions study of the network behaviour. The input of this module is a set of native data (network states and transitions in the form of values and parameters) introduced by the expert and as a result provides the inferred network composed by native and inferred transition states. This enrichment by metadata and new knowledges will facilitate decision making thanks to a powerful knowledge management. (ii) The simulation module: This module will reproduce over time the dynamic behavior of each network component. This simulator will adopt the DEVS Discrete Event Specification Formalism. (iii) The optimization module: With this module, we apply combinatorial optimization algorithms to provide a set sequences of transitions offering the best control of the network from one state to another, at the same time describing all the changes in values taking place inside each network component.
   
 
= Teaching =
 
= Teaching =
Teaching assistant at the [http://mathinfo.unistra.fr/ UFR Mathématiques-Informatique] (department of Mathematics and Computer Science) and at the [http://iutrs.unistra.fr IUT Robert Schuman] (University Institute of Technology) of the [http://unistra.fr/index.php?id=homepage University of Strasbourg].
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Teaching assistant at the [http://mathinfo.unistra.fr/ UFR Mathématiques-Informatique] (department of Mathematics and Computer Science) and at the [http://geographie.unistra.fr/ Faculté de Géographie et d'Aménagement] (University Institute of Technology) of the [http://unistra.fr/index.php?id=homepage University of Strasbourg].
   
 
'''2014/2015: '''
 
'''2014/2015: '''
* IUT Computer Science S1: Databases and SQL (10h TD/28h TP)
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* L1/MathInfo Computer Science S1: Computer and internet certificate (C2i)
* IUT Computer Science S1: Introduction to Algorithmics and Programming (26h TP)
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* Master1/GE-OTG Computer Science S2: Spatial databases and SQL (BDD - PostgreSQL)
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* L1/MathInfo Computer Science S2: Base de données (BDD - Oracle)
 
* L1/MathInfo Computer Science S2: Object-Oriented Programming (Ocaml)
   
'''2013/2014: '''
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'''2015/2016: '''
* IUT Computer Science S1: Databases and SQL (10h TD/28h TP)
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* L2/MathInfo Computer Science S1: Base de données (BDD - oracle)
* IUT Computer Science S1: Data Structures and Fundamental Algorithms (14h TD/14h TP)
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* L2/MathInfo Computer Science S1: Architecture des ordinateurs (AOD)
 
* L3/MathInfo Computer Science S2: Base de données (BDD - Oracle)
 
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* Master1/GE-OTG Computer Science S2: Spatial databases and SQL (BDD - PostgreSQL)
'''2012/2013: '''
 
* L3/S6P Mathematics: Object-Oriented Programming (18h TD/12h TP)
 
* L3/S5P Computer Science: Databases 2 (22h TP)
 
* L3/S5P Computer Science: Operating Systems Basis (12h TP)
 
   
 
= Publications =
 
= Publications =
   
<anyweb>http://icube-publis.unistra.fr/?author=Charnay&=#hideMenu</anyweb>
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<anyweb>http://icube-publis.unistra.fr/?author=AYadi_Ali&=#hideMenu</anyweb>
   
[[fr:Clément Charnay]]
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[[fr:Ali Ayadi]]

Latest revision as of 15:29, 23 December 2016

PhD student in the SDC team (formerly BFO team) of the ICube laboratory of the University of Strasbourg since May 2015.

Contact

Ali AYADI
ICube Laboratory
Télécom Physique Strasbourg
300 bd Sébastien Brant - CS 10413
F - 67412 Illkirch cedex
Office: C335
Phone: +33 (0) 6 56 76 34 46
Email: ali.ayadi (at) unistra (dot) fr

Research

PhD Thesis

Title: Semantic technologies for the optimization of complex biomolecular networks

Promotor: Cecilia Zanni-Merk (Senior Tenured Associate Professor, ICube-SDC and INSA Strasbourg)

Co-advisor: François de Bertrand de Beuvron (Tenured Associate Professor, ICube-SDC and INSA Strasbourg) and Julie Thompson (Research director CNRS, at ICube-CSTB)


Overview: This PhD thesis is prepared as part of a Franco-Tunisian cotutelle between, the SDC Team in collaboration with the LBGI Team and the University of Tunis. The general goal of this thesis is to find an optimal set of external stimuli to be applied during a predetermined time interval to evolve the network from its current state to another desired state. Our original approach is based on the combined use of semantic technologies, combinatorial optimization and simulation.

A complex biomolecular network is represented by the interactions of many molecules (genes, proteins and metabolites) in a cell. This network should stay at a normal (at least, healthy) phenotype. However, by some unknown perturbation or stimuli, the network can be transited from a normal phenotype to a disease phenotype. It thus is desirable to steer the biomolecular network to transit from the abnormal phenotype to a healthy phenotype. Here we are interested in how to effectively steer the system from an unexpected state to a desired state by applying suitable input control signals. The main purpose of this project is to provide a platform based on two strong points of the Data Mining Theme of the SDC team of ICube: semantic technologies on the one hand, and combinatorial optimisation tools on the other hand.

With this aim in mind, our future work will continue to develop a platform to study the transitions of biomolecular networks from any state to a specific state, based on three modules: (i) The ontological module: This module uses semantic technologies to generate new inferred knowledges (the discovery of new semantic associations between molecules) to refine the transitions study of the network behaviour. The input of this module is a set of native data (network states and transitions in the form of values and parameters) introduced by the expert and as a result provides the inferred network composed by native and inferred transition states. This enrichment by metadata and new knowledges will facilitate decision making thanks to a powerful knowledge management. (ii) The simulation module: This module will reproduce over time the dynamic behavior of each network component. This simulator will adopt the DEVS Discrete Event Specification Formalism. (iii) The optimization module: With this module, we apply combinatorial optimization algorithms to provide a set sequences of transitions offering the best control of the network from one state to another, at the same time describing all the changes in values taking place inside each network component.

Teaching

Teaching assistant at the UFR Mathématiques-Informatique (department of Mathematics and Computer Science) and at the Faculté de Géographie et d'Aménagement (University Institute of Technology) of the University of Strasbourg.

2014/2015:

  • L1/MathInfo Computer Science S1: Computer and internet certificate (C2i)
  • Master1/GE-OTG Computer Science S2: Spatial databases and SQL (BDD - PostgreSQL)
  • L1/MathInfo Computer Science S2: Base de données (BDD - Oracle)
  • L1/MathInfo Computer Science S2: Object-Oriented Programming (Ocaml)

2015/2016:

  • L2/MathInfo Computer Science S1: Base de données (BDD - oracle)
  • L2/MathInfo Computer Science S1: Architecture des ordinateurs (AOD)
  • L3/MathInfo Computer Science S2: Base de données (BDD - Oracle)
  • Master1/GE-OTG Computer Science S2: Spatial databases and SQL (BDD - PostgreSQL)

Publications

<anyweb>http://icube-publis.unistra.fr/?author=AYadi_Ali&=#hideMenu</anyweb>