Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/61781
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Sampaio, Marta | por |
dc.contributor.author | Rocha, Miguel | por |
dc.contributor.author | Oliveira, Hugo Alexandre Mendes | por |
dc.contributor.author | Dias, Oscar | por |
dc.date.accessioned | 2019-10-14T09:30:53Z | - |
dc.date.available | 2019-10-14T09:30:53Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Sampaio, Marta; Rocha, Miguel; Oliveira, Hugo; Dias, Oscar, Predicting promoters in phage genomes using machine learning models. Advances in Intelligent Systems and Computing. Vol. 1005 (PACBB 2019), Springer, 105-112, 2020. | por |
dc.identifier.isbn | 9783030238728 | por |
dc.identifier.issn | 2194-5357 | por |
dc.identifier.uri | https://hdl.handle.net/1822/61781 | - |
dc.description.abstract | The renewed interest in phages as antibacterial agents has led to the exponentially growing number of sequenced phage genomes. Therefore, the development of novel bioinformatics methods to automate and facilitate phage genome annotation is of utmost importance. The most difficult step of phage genome annotation is the identification of promoters. As the existing methods for predicting promoters are not well suited for phages, we used machine learning models for locating promoters in phage genomes. Several models were created, using different algorithms and datasets, which consisted of known phage promoter and non-promoter sequences. All models showed good performance, but the ANN model provided better results for the smaller dataset (92% of accuracy, 89% of precision and 87% of recall) and the SVM model returned better results for the larger dataset (93% of accuracy, 91% of precision and 80% of recall). Both models were applied to the genome of Pseudomonas phage phiPsa17 and were able to identify both types of promoters, host and phage, found in phage genomes. | por |
dc.description.sponsorship | This study was supported by the Portuguese Foundation for Science andTechnology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and theProject POCI-01-0145-FEDER-029628. This work was also supported by BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fundunder the scope of Norte2020 - Programa Operacional Regional do Norte. | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | UID/BIO/04469/2019 | por |
dc.rights | openAccess | por |
dc.subject | Machine learning | por |
dc.subject | Genome analysis | por |
dc.subject | Phages Promoters | por |
dc.subject | Phages | por |
dc.subject | Promoters | por |
dc.title | Predicting promoters in phage genomes using machine learning models | por |
dc.type | conferencePaper | - |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://www.springer.com/series/11156 | por |
dc.comments | CEB51780 | por |
oaire.citationStartPage | 105 | por |
oaire.citationEndPage | 112 | por |
oaire.citationVolume | 1005 | por |
dc.date.updated | 2019-09-28T12:36:38Z | - |
dc.identifier.eissn | 2194-5365 | por |
dc.identifier.doi | 10.1007/978-3-030-23873-5_13 | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | - |
dc.subject.wos | Science & Technology | por |
sdum.journal | Advances in Intelligent Systems and Computing | por |
sdum.conferencePublication | PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | por |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_51780_1.pdf | 424,63 kB | Adobe PDF | Ver/Abrir |