An empirical comparison of popular algorithms for learning gene networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00450559" target="_blank" >RIV/67985556:_____/15:00450559 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
An empirical comparison of popular algorithms for learning gene networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 10th Workshop on Uncertainty Processing WUPES?15
ISBN
978-80-245-2102-2
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
61-72
Publisher name
Oeconomica
Place of publication
Praha
Event location
Monínec
Event date
Sep 16, 2015
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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