bibtype C - Conference Paper (international conference)
ARLID 0450559
utime 20240103211213.5
mtime 20151201235959.9
title (primary) (eng) An empirical comparison of popular algorithms for learning gene networks
specification
page_count 12 s.
media_type E
serial
ARLID cav_un_epca*0447898
ISBN 978-80-245-2102-2
title Proceedings of the 10th Workshop on Uncertainty Processing WUPES’15
page_num 61-72
publisher
place Praha
name Oeconomica
year 2015
editor
name1 Kratochvíl
name2 V.
keyword Bayesian networks
keyword Gene networks
keyword Biological pathways
author (primary)
ARLID cav_un_auth*0322154
name1 Djordjilović
name2 V.
country IT
author
ARLID cav_un_auth*0322155
name1 Chiogna
name2 M.
country IT
author
ARLID cav_un_auth*0101228
name1 Vomlel
name2 Jiří
full_dept (cz) Matematická teorie rozhodování
full_dept Department of Decision Making Theory
department (cz) MTR
department MTR
institution UTIA-B
full_dept Department of Decision Making Theory
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2015/MTR/vomlel-0450559.pdf
cas_special
abstract (eng) 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.
action
ARLID cav_un_auth*0319735
name WUPES 2015. Workshop on Uncertainty Processing /10./
place Monínec
dates 16.09.2015-19.09.2015
country CZ
reportyear 2016
RIV IN
num_of_auth 3
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0252671
confidential S
arlyear 2015
mrcbU63 cav_un_epca*0447898 Proceedings of the 10th Workshop on Uncertainty Processing WUPES’15 978-80-245-2102-2 61 72 Praha Oeconomica 2015
mrcbU67 Kratochvíl V. 340