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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 - UMR 5558
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.
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https://hal.inria.fr/hal-00697024
Contributor : David Parsons <>
Submitted on : Monday, May 14, 2012 - 1:45:07 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:50 PM

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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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