An empirical comparison of popular structure learning algorithms with a view to gene network inference

0477168 - ÚTIA 2018 RIV US eng J - Journal Article
Djordjilović, V. - Chiogna, M. - Vomlel, Jiří
An empirical comparison of popular structure learning algorithms with a view to gene network inference.
International Journal of Approximate Reasoning. Roč. 88, č. 1 (2017), s. 602-613. ISSN 0888-613X. E-ISSN 1873-4731
R&D Projects: GA ČR(CZ) GA16-12010S
Institutional support: RVO:67985556
Keywords : Bayesian networks * Structure learning * Reverse engineering * Gene networks
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 1.766, year: 2017
http://library.utia.cas.cz/separaty/2017/MTR/vomlel-0477168.pdf

In this work, we study the performance of different structure learning algorithms in the context of inferring gene networks from transcription 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. Areal data application based on an experiment performed by the University of Padova is also considered.
Permanent Link: http://hdl.handle.net/11104/0273649