Original title: An empirical comparison of popular algorithms for learning gene networks
Authors: Djordjilović, V. ; Chiogna, M. ; Vomlel, Jiří
Document type: Papers
Conference/Event: WUPES 2015. Workshop on Uncertainty Processing /10./, Monínec (CZ), 2015-09-16 / 2015-09-19
Year: 2015
Language: eng
Abstract: 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.
Keywords: Bayesian networks; Biological pathways; Gene networks
Host item entry: Proceedings of the 10th Workshop on Uncertainty Processing WUPES’15, ISBN 978-80-245-2102-2

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2015/MTR/vomlel-0450559.pdf
Original record: http://hdl.handle.net/11104/0252671

Permalink: http://www.nusl.cz/ntk/nusl-201145


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