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Optimization of RNA classification using the resonant recognition model

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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Ciências Biológicas
dc.contributor.authorSouza, Felipe
dc.contributor.authorPimenta-Zanon, Matheus
dc.contributor.authorHenriques, Dora
dc.contributor.authorPinto, M. Alice
dc.contributor.authorBalsa, Carlos
dc.contributor.authorRufino, José
dc.contributor.authorLopes, Fabrı́cio
dc.date.accessioned2026-03-16T10:02:34Z
dc.date.available2026-03-16T10:02:34Z
dc.date.issued2024
dc.description.abstractThe development of high throughput sequencing technologies, such as RNA-Seq, has enabled the generation of large volumes of biological data. Thus, it is necessary to develop computational methods to interpret this massive volume of data and contribute to knowledge discovery. RNA sequences are products of the transcription of genomic DNA sequences, and represent the gene expression process that organisms use to synthesize protein or RNA molecules. These RNA sequences can be compared between organisms of the same or different species to demonstrate similar functional proteins. The RNA sequences have different biological functions, such as mRNAs, rRNAs, tRNAs and ncRNA, to name a few. The correct identification of each class of RNA sequences is important because of the huge volume of unlabeled data available. In this context, this study proposes an approach based on the Resonant Recognition Model for feature extraction and classification regarding the ncRNA and mRNA classes. To assess the proposed approach, it was adopted the dataset from the PLEK method. Despite the reduction of the input data size achieved using the RMM method, the results show high accuracy, indicating the potential of the proposed approach.por
dc.identifier.citationSouza, Felipe; Pimenta-Zanon, Matheus; Henriques, Dora; Pinto, M. Alice; Balsa, Carlos; Lopes, Fabrício (2024). Optimization of RNA classification using the resonant recognition model. In 4th Symposium of Applied Science for Young Researchers (SASYR 2024). Bragança, Portugal. p. 1-8. ISBN 978-972-745-341-2
dc.identifier.isbn978-972-745-341-2
dc.identifier.urihttp://hdl.handle.net/10198/36078
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstituto Politécnico de Bragança
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relation.hasversionhttp://hdl.handle.net/10198/30840
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRNA
dc.subjectRRM
dc.subjectRNAs classification
dc.subjectFeature Extraction
dc.subjectBioinformatics
dc.subjectPattern Recognition
dc.titleOptimization of RNA classification using the resonant recognition modeleng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDP/05757/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.citation.conferenceDate2024
oaire.citation.conferencePlaceBragança, Portugal
oaire.citation.endPage8
oaire.citation.startPage1
oaire.citation.title4th Symposium of Applied Science for Young Researchers (SASYR 2024)
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameHenriques
person.familyNamePinto
person.familyNameBalsa
person.familyNameRufino
person.givenNameDora
person.givenNameM. Alice
person.givenNameCarlos
person.givenNameJosé
person.identifier1721518
person.identifier.ciencia-id291F-986F-07DA
person.identifier.ciencia-idF814-A1D0-8318
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.ciencia-idC414-F47F-6323
person.identifier.orcid0000-0001-7530-682X
person.identifier.orcid0000-0001-9663-8399
person.identifier.orcid0000-0003-2431-8665
person.identifier.orcid0000-0002-1344-8264
person.identifier.ridM-8735-2013
person.identifier.scopus-author-id55761737300
person.identifier.scopus-author-id8085507800
person.identifier.scopus-author-id23391719100
person.identifier.scopus-author-id55947199100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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