From Digital Genetics to Knowledge Discovery: Perspectives in Genetic Network Understanding

Guillaume Beslon 1, 2, 3 David P. Parsons 3, 1, 2 Jose-Maria Pena Christophe Rigotti 4, 3, 1, 2 Yolanda Sanchez-Dehesa 3, 2
1 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive, CarMeN - Cardiovasculaire, métabolisme, diabétologie et nutrition
3 COMBINING - COMputational BIology and data miNING
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes
4 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper, we propose an original computational approach to assist knowledge discovery in complex biological networks. First, we present an integrated model of the evolution of regulation networks that can be used to uncover organization principles of such networks. Then, we propose to use the results of our model as a benchmark for knowledge discovery algorithms. We describe a first experiment of such benchmarking by using gene knock-out data generated from the modeled organisms.
Type de document :
Article dans une revue
Intelligent Data Analysis, IOS Press, 2010, 14 (2), pp.173--191. 〈10.3233/IDA-2010-0415〉
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https://hal.inria.fr/hal-00697024
Contributeur : David Parsons <>
Soumis le : lundi 14 mai 2012 - 13:45:07
Dernière modification le : jeudi 1 novembre 2018 - 01:20:36

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Guillaume Beslon, David P. Parsons, Jose-Maria Pena, Christophe Rigotti, Yolanda Sanchez-Dehesa. From Digital Genetics to Knowledge Discovery: Perspectives in Genetic Network Understanding. Intelligent Data Analysis, IOS Press, 2010, 14 (2), pp.173--191. 〈10.3233/IDA-2010-0415〉. 〈hal-00697024〉

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