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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 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název An empirical comparison of popular structure learning algorithms with a view to gene network inference Tvůrce(i) Djordjilović, V. (IT)
Chiogna, M. (IT)
Vomlel, Jiří (UTIA-B) RID, ORCIDCelkový počet autorů 3 Zdroj.dok. International Journal of Approximate Reasoning. - : Elsevier - ISSN 0888-613X
Roč. 88, č. 1 (2017), s. 602-613Poč.str. 14 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova Bayesian networks ; Structure learning ; Reverse engineering ; Gene networks Vědní obor RIV JD - Využití počítačů, robotika a její aplikace Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA16-12010S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 UT WOS 000407655600031 EID SCOPUS 85009223244 DOI 10.1016/j.ijar.2016.12.012 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2018
Počet záznamů: 1