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A scalable genetic programming approach to integrate miRNA-target predictions

2018-01-01Artigo científico Acesso aberto
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dc.contributor.authorBeretta, Stefano
dc.contributor.authorCastelli, Mauro
dc.contributor.authorMuñoz, Luis
dc.contributor.authorTrujillo, Leonardo
dc.contributor.authorMartínez, Yuliana
dc.contributor.authorPopovič, Aleš
dc.contributor.authorMilanesi, Luciano
dc.contributor.authorMerelli, Ivan
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblHindawi Publishing Corporation
dc.date.accessioned2019-03-25T23:13:09Z
dc.date.available2019-03-25T23:13:09Z
dc.date.issued2018-01-01
dc.descriptionBeretta, 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
dc.description.abstractThere 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2613046
dc.identifier.doi10.1155/2018/4963139
dc.identifier.issn1076-2787
dc.identifier.otherPURE: 12355656
dc.identifier.otherPURE UUID: cc47ed55-0d77-45d7-82d4-c1990dc79b12
dc.identifier.otherScopus: 85062830331
dc.identifier.otherWOS: 000438805500001
dc.identifier.otherORCID: /0000-0002-8793-1451/work/72856214
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85062830331&partnerID=8YFLogxK
dc.identifier.urlhttps://www.scopus.com/pages/publications/85062830331
dc.language.isoeng
dc.peerreviewedyes
dc.subjectGeneral
dc.titleA scalable genetic programming approach to integrate miRNA-target predictionsen
dc.title.subtitleComparing different parallel implementations of M3GPen
dc.typejournal article
degois.publication.titleComplexity
degois.publication.volume2018
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)