A Data Mining Approach to Discover Genetic ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
A Data Mining Approach to Discover Genetic and Environmental Factors involved in Multifactoral Diseases
Auteur(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]
Titre de la revue :
Knowledge-Based Systems
Pagination :
235--242
Éditeur :
Elsevier
Date de publication :
2002-05
ISSN :
0950-7051
Mot(s)-clé(s) en anglais :
Data mining
Clustering
Genetic algorithm
Feature selection
Multifactorial disease
Clustering
Genetic algorithm
Feature selection
Multifactorial disease
Discipline(s) HAL :
Mathématiques [math]/Combinatoire [math.CO]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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