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

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    SYSNO ASEP0477168
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleAn 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, ORCID
    Number of authors3
    Source TitleInternational Journal of Approximate Reasoning. - : Elsevier - ISSN 0888-613X
    Roč. 88, č. 1 (2017), s. 602-613
    Number of pages14 s.
    Languageeng - English
    CountryUS - United States
    KeywordsBayesian networks ; Structure learning ; Reverse engineering ; Gene networks
    Subject RIVJD - Computer Applications, Robotics
    OECD categoryComputer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    R&D ProjectsGA16-12010S GA ČR - Czech Science Foundation (CSF)
    Institutional supportUTIA-B - RVO:67985556
    UT WOS000407655600031
    EID SCOPUS85009223244
    DOI10.1016/j.ijar.2016.12.012
    AnnotationIn 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.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Year of Publishing2018
Number of the records: 1  

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