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Conference Paper (international conference)

An empirical comparison of popular algorithms for learning gene networks

Djordjilović V., Chiogna M., Vomlel Jiří

: Proceedings of the 10th Workshop on Uncertainty Processing WUPES’15, p. 61-72 , Eds: Kratochvíl V.

: WUPES 2015. Workshop on Uncertainty Processing /10./, (Monínec, CZ, 16.09.2015-19.09.2015)

: Bayesian networks, Gene networks, Biological pathways

: http://library.utia.cas.cz/separaty/2015/MTR/vomlel-0450559.pdf

(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.

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