Identifying breast cancer cell lines using high performance machine learning methods

Página do item simplificado
dc.contributor.authorFAROOQ, SAJIDpt_BR
dc.contributor.authorDEL-VALLE, MATHEUSpt_BR
dc.contributor.authorSANTOS, SOFIApt_BR
dc.contributor.authorBERNARDES, EMERSON S.pt_BR
dc.contributor.authorZEZELL, DENISE M.pt_BR
dc.coverageInternacionalpt_BR
dc.creator.eventoLATIN AMERICA OPTICS AND PHOTONICS CONFERENCEpt_BR
dc.date.accessioned2023-02-06T18:00:19Z
dc.date.available2023-02-06T18:00:19Z
dc.date.eventoAugust 7-11, 2022pt_BR
dc.description.abstractWe present a computational framework based on machine learning classifiers K-Nearest Neighbors and Neighborhood Component analysis for breast cancer (BC) subtypes prognostic. Our results has up to 97% accuracy for prognostic stratification of BC subtypes.pt_BR
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorshipIDFAPESP: 17/50332-0pt_BR
dc.description.sponsorshipIDCNPq: INCT-465763/2014-6; PQ-31451712021-9; 142229/2019-9pt_BR
dc.event.siglaLAOPpt_BR
dc.identifier.citationFAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M. Identifying breast cancer cell lines using high performance machine learning methods. In: LATIN AMERICA OPTICS AND PHOTONICS CONFERENCE, August 7-11, 2022, Recife, PE. <b>Proceedings...</b> Washington, DC, USA: Optica Publishing Group, 2022. DOI: <a href="https://dx.doi.org/10.1364/LAOP.2022.Tu5A.3">10.1364/LAOP.2022.Tu5A.3</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/33726.
dc.identifier.doi10.1364/LAOP.2022.Tu5A.3pt_BR
dc.identifier.orcid0000-0001-7404-9606pt_BR
dc.identifier.orcid0000-0002-0029-7313pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-7404-9606
dc.identifier.orcidhttps://orcid.org/0000-0002-0029-7313
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/33726
dc.localWashington, DC, USApt_BR
dc.local.eventoRecife, PEpt_BR
dc.publisherOptica Publishing Grouppt_BR
dc.rightsopenAccesspt_BR
dc.subjectprogramming
dc.subjectcomputer codes
dc.subjectautomation
dc.subjectmachine learning
dc.subjectneoplasms
dc.subjectmammary glands
dc.subjectdiagnosis
dc.subjecttumor cells
dc.titleIdentifying breast cancer cell lines using high performance machine learning methodspt_BR
dc.typeTexto completo de eventopt_BR
dspace.entity.typePublication
ipen.autorSAJID FAROOQ
ipen.autorSOFIA NASCIMENTO DOS SANTOS
ipen.autorDENISE MARIA ZEZELL
ipen.autorEMERSON SOARES BERNARDES
ipen.autorMATHEUS DEL VALLE
ipen.codigoautor15722
ipen.codigoautor14464
ipen.codigoautor693
ipen.codigoautor12099
ipen.codigoautor15209
ipen.contributor.ipenauthorSAJID FAROOQ
ipen.contributor.ipenauthorSOFIA NASCIMENTO DOS SANTOS
ipen.contributor.ipenauthorDENISE MARIA ZEZELL
ipen.contributor.ipenauthorEMERSON SOARES BERNARDES
ipen.contributor.ipenauthorMATHEUS DEL VALLE
ipen.date.recebimento23-02
ipen.event.datapadronizada2022pt_BR
ipen.identifier.ipendoc29360pt_BR
ipen.identifier.ods3
ipen.notas.internasProceedingspt_BR
ipen.type.genreArtigo
relation.isAuthorOfPublication60d3fba4-40e1-482c-9eda-4530bc63fecb
relation.isAuthorOfPublicationab78881a-78eb-42be-a463-aaf80e70de3d
relation.isAuthorOfPublicationa565f8ad-3432-4891-98c0-a587f497db21
relation.isAuthorOfPublication8115c8bd-822c-4f5a-9f49-3c12570ed40a
relation.isAuthorOfPublicationfdd01116-8cc4-406a-aafb-606941dc28dc
relation.isAuthorOfPublication.latestForDiscovery60d3fba4-40e1-482c-9eda-4530bc63fecb
sigepi.autor.atividadeZEZELL, DENISE M.:693:920:Npt_BR
sigepi.autor.atividadeBERNARDES, EMERSON S.:12099:110:Npt_BR
sigepi.autor.atividadeSANTOS, SOFIA:14464:110:Npt_BR
sigepi.autor.atividadeDEL-VALLE, MATHEUS:15209:920:Npt_BR
sigepi.autor.atividadeFAROOQ, SAJID:15722:920:Spt_BR
Pacote Original
Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
29360.pdf
Tamanho:
234.65 KB
Formato:
Adobe Portable Document Format
Descrição:
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar
Coleções
Eventos