0450559 20240103211213.520151201235959.9 12 s. E cav_un_epca*0447898978-80-245-2102-261-72PrahaOeconomica2015KratochvílV. Bayesian networks Gene networks Biological pathways cav_un_auth*0322154 Djordjilović V. IT cav_un_auth*0322155 Chiogna M. IT cav_un_auth*0101228 Vomlel Jiří Matematická teorie rozhodování Department of Decision Making Theory MTR MTR UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/MTR/vomlel-0450559.pdf In this work, we study the performance of different algorithms for learning gene networks from data. We consider representatives of different structure learning approaches, some of which perform unrestricted searches, such as the PC algorithm and the Gobnilp method and some of which introduce prior information on the structure, such as the K2 algorithm. Competing methods are evaluated both in terms of their predictive accuracy and their ability to reconstruct the true underlying network. A real data application based on an experiment performed by the University of Padova is also considered. We also discuss merits and disadvantages of categorizing gene expression measurements. cav_un_auth*0319735 WUPES 2015. Workshop on Uncertainty Processing /10./ Monínec 16.09.2015-19.09.2015 CZ 2016 IN 3 PR RVO:67985556 http://hdl.handle.net/11104/0252671 S 2015 cav_un_epca*0447898 Proceedings of the 10th Workshop on Uncertainty Processing WUPES’15 978-80-245-2102-2 61 72 Praha Oeconomica 2015 Kratochvíl V. 340