From digital genetics to knowledge discovery: Perspectives in genetic network understanding

Issue title: Knowledge Discovery in Bioinformatics

Guest editors: José-María Peñax and Evgenii Evgeniiy

Article type: Research Article

Authors: Beslon, Guillaume^{a; c; *; 1} | Parsons, David P.^{a; c; 1} | Peña, Jose-María^b | Rigotti, Christophe^{a; c; 1} | Sanchez-Dehesa, Yolanda^{a; c; 1}

Affiliations: [a] Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, F-69621, France | [b] DATSI, Universidad Politecnica de Madrid, Spain | [c] IXXI, Institut Rhône-Alpin des Systèmes Complexes, Lyon, F-69007, France | [x] Universidad Politécnica de Madrid, Spain | [y] Sobolev Institute of Mathematics, Russia

Correspondence: [*] Corresponding author. E-mail: [email protected].

Note: [1] On strike while writing this article to protest against French Government’s projects on research management.

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.

DOI: 10.3233/IDA-2010-0415

Journal: Intelligent Data Analysis, vol. 14, no. 2, pp. 173-191, 2010