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Resonant recognition model as a preprocessing technique for RNA classification

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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosCiências Naturais::Ciências Biológicas
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorSouza, Felipe Bueno de
dc.contributor.authorPimenta-Zanon, Matheus Henrique
dc.contributor.authorHenriques, Dora
dc.contributor.authorPinto, M. Alice
dc.contributor.authorBalsa, Carlos
dc.contributor.authorRufino, José
dc.contributor.authorLopes, Fabrício Martins
dc.date.accessioned2026-03-18T11:54:57Z
dc.date.available2026-03-18T11:54:57Z
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 Fundação Araucária (Grant number 035/2019, 138/2021 and NAPI -Bioinformática), CNPq 440412/2022-6 and 408312/2023-8), FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI:10.54499/UIDB/05757/2020)and UIDB/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 Henrique; Henriques, Dora; Pinto, M. Alice; Balsa, Carlos; Rufino, José; Lopes, Fabrício Martins (2025). Resonant recognition model as a preprocessing technique for RNA classification. In International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024. ISSN 1865-0929. 2348 CCIS, p. 3-17. DOI: 10.1007/978-3-031-83435-6_1
dc.identifier.doi10.1007/978-3-031-83435-6_1
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10198/36120
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationMountain Research Center
dc.relationMountain Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofAdvanced Research in Technologies, Information, Innovation and Sustainability
dc.rights.urihttp://creativecommons.org/licenses/by-nd/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 classificationeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberUIDB/00690/2020
oaire.awardNumberUIDP/00690/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleMountain Research Center
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/UIDP%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage17
oaire.citation.startPage3
oaire.citation.titleInternational Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2024
oaire.citation.volume2348 CCIS
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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
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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
project.funder.nameFundação para a Ciência e a Tecnologia
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