An empirical comparison of popular algorithms for learning gene networks

0450559 - ÚTIA 2016 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Djordjilović, V. - Chiogna, M. - Vomlel, Jiří
An empirical comparison of popular algorithms for learning gene networks.
Proceedings of the 10th Workshop on Uncertainty Processing WUPES’15. Praha: Oeconomica, 2015 - (Kratochvíl, V.), s. 61-72. ISBN 978-80-245-2102-2.
[WUPES 2015. Workshop on Uncertainty Processing /10./. Monínec (CZ), 16.09.2015-19.09.2015]
Institucionální podpora: RVO:67985556
Klíčová slova: Bayesian networks * Gene networks * Biological pathways
Kód oboru RIV: IN - Informatika
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.
Trvalý link: http://hdl.handle.net/11104/0252671