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An empirical comparison of popular structure learning algorithms with a view to gene network inference
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SYSNO ASEP 0477168 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title An empirical comparison of popular structure learning algorithms with a view to gene network inference Author(s) Djordjilović, V. (IT)
Chiogna, M. (IT)
Vomlel, Jiří (UTIA-B) RID, ORCIDNumber of authors 3 Source Title International Journal of Approximate Reasoning. - : Elsevier - ISSN 0888-613X
Roč. 88, č. 1 (2017), s. 602-613Number of pages 14 s. Language eng - English Country US - United States Keywords Bayesian networks ; Structure learning ; Reverse engineering ; Gene networks Subject RIV JD - Computer Applications, Robotics OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA16-12010S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000407655600031 EID SCOPUS 85009223244 DOI 10.1016/j.ijar.2016.12.012 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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