A scalable genetic programming approach to integrate miRNA-target predictions

Detalhes bibliográficos
Autor(a) principal: Beretta, Stefano
Data de Publicação: 2018
Outros Autores: Castelli, Mauro, Muñoz, Luis, Trujillo, Leonardo, Martínez, Yuliana, Popovič, Aleš, Milanesi, Luciano, Merelli, Ivan
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1155/2018/4963139
Resumo: Beretta, S., Castelli, M., Munoz, L., Trujillo, L., Martinez, Y., Popovic, A., ... Merelli, I. (2018). A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP. Complexity, [4963139]. DOI: 10.1155/2018/4963139
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spelling A scalable genetic programming approach to integrate miRNA-target predictionsComparing different parallel implementations of M3GPGeneralBeretta, S., Castelli, M., Munoz, L., Trujillo, L., Martinez, Y., Popovic, A., ... Merelli, I. (2018). A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP. Complexity, [4963139]. DOI: 10.1155/2018/4963139There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNBeretta, StefanoCastelli, MauroMuñoz, LuisTrujillo, LeonardoMartínez, YulianaPopovič, AlešMilanesi, LucianoMerelli, Ivan2019-03-25T23:13:09Z2018-01-012018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.1155/2018/4963139eng1076-2787PURE: 12355656http://www.scopus.com/inward/record.url?scp=85062830331&partnerID=8YFLogxKhttps://doi.org/10.1155/2018/4963139info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:30:36Zoai:run.unl.pt:10362/64537Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:34:08.260179Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A scalable genetic programming approach to integrate miRNA-target predictions
Comparing different parallel implementations of M3GP
title A scalable genetic programming approach to integrate miRNA-target predictions
spellingShingle A scalable genetic programming approach to integrate miRNA-target predictions
Beretta, Stefano
General
title_short A scalable genetic programming approach to integrate miRNA-target predictions
title_full A scalable genetic programming approach to integrate miRNA-target predictions
title_fullStr A scalable genetic programming approach to integrate miRNA-target predictions
title_full_unstemmed A scalable genetic programming approach to integrate miRNA-target predictions
title_sort A scalable genetic programming approach to integrate miRNA-target predictions
author Beretta, Stefano
author_facet Beretta, Stefano
Castelli, Mauro
Muñoz, Luis
Trujillo, Leonardo
Martínez, Yuliana
Popovič, Aleš
Milanesi, Luciano
Merelli, Ivan
author_role author
author2 Castelli, Mauro
Muñoz, Luis
Trujillo, Leonardo
Martínez, Yuliana
Popovič, Aleš
Milanesi, Luciano
Merelli, Ivan
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Beretta, Stefano
Castelli, Mauro
Muñoz, Luis
Trujillo, Leonardo
Martínez, Yuliana
Popovič, Aleš
Milanesi, Luciano
Merelli, Ivan
dc.subject.por.fl_str_mv General
topic General
description Beretta, S., Castelli, M., Munoz, L., Trujillo, L., Martinez, Y., Popovic, A., ... Merelli, I. (2018). A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP. Complexity, [4963139]. DOI: 10.1155/2018/4963139
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2018-01-01T00:00:00Z
2019-03-25T23:13:09Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.1155/2018/4963139
url https://doi.org/10.1155/2018/4963139
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1076-2787
PURE: 12355656
http://www.scopus.com/inward/record.url?scp=85062830331&partnerID=8YFLogxK
https://doi.org/10.1155/2018/4963139
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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