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Resonant recognition model as a preprocessing technique for RNA classification

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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Ciências Biológicas
dc.contributor.authorSouza, Felipe Bueno de
dc.contributor.authorPimenta-Zanon, Matheus
dc.contributor.authorHenriques, Dora
dc.contributor.authorPinto, M. Alice
dc.contributor.authorBalsa, Carlos
dc.contributor.authorRufino, José
dc.contributor.authorFabrício Martins Lopes
dc.contributor.editor.
dc.date.accessioned2026-03-17T10:07:42Z
dc.date.available2026-03-17T10:07:42Z
dc.date.issued2025
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. There are several classes of RNA sequences (mRNA, rRNA, tRNA, ncRNA, etc.), with different biological functions. The correct identification of each class of RNA sequences is important because of the huge volume of unlabelled data available. In this context, this study proposes an approach based on the Resonant Recognition Model (RRM) 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 RRM model, the results show high accuracy for primary protein sequences translated from RNA sequences, signaling the potential of the proposed approach to classify RNA.eng
dc.description.sponsorshipThis work was supported by national funds through the Funda\u00E7\u00E3o Arauc\u00E1ria (Grant number 035/2019, 138/2021 and NAPI - Bioinform\u00E1tica), CNPq440412/2022-6 and 408312/2023-8), FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020); CIMO, UIDB/00690/2020 (DOI: 10.54499/UIDB/00690/2020) and UIDP/00690/2020 (DOI: 10.54499/UIDP/00690/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/ LA/P/0007/2020).
dc.identifier.citationSouza, Felipe Bueno de; Pimenta-Zanon, Matheus; Henriques, Dora; Pinto, M. Alice; Balsa, Carlos; Rufino, José; Fabrício Martins Lopes (2025). Resonant recognition model as a preprocessing technique for RNA classification. In ARTIIS 2024 International Workshops. 2348:1, p. 3-17. ISSN 1865-0929. DOI: 10.1007/978-3-031-83435-6_1
dc.identifier.doi10.1007/978-3-031-83435-6_1
dc.identifier.isbn9783031834356
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10198/36095
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature Switzerland
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationMountain Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-031-83435-6_1
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofAdvanced Research in Technologies, Information, Innovation and Sustainability
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRNA
dc.subjectDNA
dc.subjectAmino Acid
dc.subjectRRM
dc.subjectRNAs Classification
dc.subjectFeature Extraction
dc.subjectBioinformatics
dc.subjectPattern Recognition
dc.titleResonant recognition model as a preprocessing technique for RNA classificationpor
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberUIDB/00690/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleMountain Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage17
oaire.citation.issue1
oaire.citation.startPage3
oaire.citation.titleARTIIS 2024 International Workshops
oaire.citation.volume2348
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePinto
person.familyNameBalsa
person.familyNameRufino
person.givenNameM. Alice
person.givenNameCarlos
person.givenNameJosé
person.identifier1721518
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person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.ciencia-idC414-F47F-6323
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-id8085507800
person.identifier.scopus-author-id23391719100
person.identifier.scopus-author-id55947199100
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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