A Data Mining Approach to Discover Genetic ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
A Data Mining Approach to Discover Genetic and Environmental Factors involved in Multifactoral Diseases
Author(s) :
Jourdan, Laetitia [Auteur correspondant]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur correspondant]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Gallina, Sophie [Auteur]
Institut de biologie de Lille - UMS 3702 [IBL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur correspondant]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Gallina, Sophie [Auteur]
Institut de biologie de Lille - UMS 3702 [IBL]
Journal title :
Knowledge-Based Systems
Pages :
235--242
Publisher :
Elsevier
Publication date :
2002-05
ISSN :
0950-7051
English keyword(s) :
Data mining
Clustering
Genetic algorithm
Feature selection
Multifactorial disease
Clustering
Genetic algorithm
Feature selection
Multifactorial disease
HAL domain(s) :
Mathématiques [math]/Combinatoire [math.CO]
English abstract : [en]
In this paper, we are interested in discovering genetic factors that are involved in multifactorial diseases. Therefore, experiments have been achieved by the Biological Institute of Lille and a lot of data has been ...
Show more >In this paper, we are interested in discovering genetic factors that are involved in multifactorial diseases. Therefore, experiments have been achieved by the Biological Institute of Lille and a lot of data has been generated. To exploit this data, data mining tools are required and we propose a 2-phase optimization approach using a specific genetic algorithm. During the first step, we select significant features with a specific genetic algorithm. Then, during the second step, we cluster affected individuals according to the features selected by the first phase. The paper describes the specificities of the genetic problem that we are studying and presents in details the genetic algorithm that we have developed to deal with this very large size problem of feature selection. Results on both artificial and real data are presented.Show less >
Show more >In this paper, we are interested in discovering genetic factors that are involved in multifactorial diseases. Therefore, experiments have been achieved by the Biological Institute of Lille and a lot of data has been generated. To exploit this data, data mining tools are required and we propose a 2-phase optimization approach using a specific genetic algorithm. During the first step, we select significant features with a specific genetic algorithm. Then, during the second step, we cluster affected individuals according to the features selected by the first phase. The paper describes the specificities of the genetic problem that we are studying and presents in details the genetic algorithm that we have developed to deal with this very large size problem of feature selection. Results on both artificial and real data are presented.Show less >
Language :
Anglais
Popular science :
Non
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