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BNFinder2: Faster Bayesian network learning and Bayesian classification

Summary: Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact al...

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Autores principales: Dojer, Norbert, Bednarz, Paweł, Podsiadło, Agnieszka, Wilczyński, Bartek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722519/
https://www.ncbi.nlm.nih.gov/pubmed/23818512
http://dx.doi.org/10.1093/bioinformatics/btt323
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author Dojer, Norbert
Bednarz, Paweł
Podsiadło, Agnieszka
Wilczyński, Bartek
author_facet Dojer, Norbert
Bednarz, Paweł
Podsiadło, Agnieszka
Wilczyński, Bartek
author_sort Dojer, Norbert
collection PubMed
description Summary: Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. Availability and implementation: BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user’s manual, introductory tutorial and supplementary methods. Contact: dojer@mimuw.edu.pl or bartek@mimuw.edu.pl Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-37225192013-07-25 BNFinder2: Faster Bayesian network learning and Bayesian classification Dojer, Norbert Bednarz, Paweł Podsiadło, Agnieszka Wilczyński, Bartek Bioinformatics Applications Notes Summary: Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. Availability and implementation: BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user’s manual, introductory tutorial and supplementary methods. Contact: dojer@mimuw.edu.pl or bartek@mimuw.edu.pl Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-08-15 2013-07-01 /pmc/articles/PMC3722519/ /pubmed/23818512 http://dx.doi.org/10.1093/bioinformatics/btt323 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Dojer, Norbert
Bednarz, Paweł
Podsiadło, Agnieszka
Wilczyński, Bartek
BNFinder2: Faster Bayesian network learning and Bayesian classification
title BNFinder2: Faster Bayesian network learning and Bayesian classification
title_full BNFinder2: Faster Bayesian network learning and Bayesian classification
title_fullStr BNFinder2: Faster Bayesian network learning and Bayesian classification
title_full_unstemmed BNFinder2: Faster Bayesian network learning and Bayesian classification
title_short BNFinder2: Faster Bayesian network learning and Bayesian classification
title_sort bnfinder2: faster bayesian network learning and bayesian classification
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722519/
https://www.ncbi.nlm.nih.gov/pubmed/23818512
http://dx.doi.org/10.1093/bioinformatics/btt323
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