Breast cancer subtypes diagnostic via high performance supervised machine learning

Página do item simplificado
dc.contributor.authorFAROOQ, SAJIDpt_BR
dc.contributor.authorDEL-VALLE, MATHEUSpt_BR
dc.contributor.authorSANTOS, MOISES O. dospt_BR
dc.contributor.authorNASCIMENTO, SOFIApt_BR
dc.contributor.authorBERNARDES, EMERSON S.pt_BR
dc.contributor.authorZEZELL, DENISE M.pt_BR
dc.coverageInternacionalpt_BR
dc.creator.eventoINTERNATIONAL CONFERENCE ON CLINICAL SPECTROSCOPY, 12thpt_BR
dc.date.accessioned2023-03-24T17:45:07Z
dc.date.available2023-03-24T17:45:07Z
dc.date.eventoJune 19-23, 2022pt_BR
dc.description.abstractAim: Breast cancer molecular subtypes are being used to improve clinical decision. The Fourier transform infrared (FTIR) spectroscopic imaging, which is a powerful and non-destructive technique, allows performing a non-perturbative and labelling free extraction of biochemical information towards diagnosis and evaluation for cell functionality. However, methods of measurements of large areas of cells demand a long time to achieve high quality images, making its clinical use impractical because of speed of data acquisition and dearth of optimized computational procedures. In order to cope with these challenges, Machine learning (ML) technologies can facilitate to obtain accurate prognosis of Breast Cancer (BC) subtypes with high action ability and accuracy. Methods: Here we propose a ML algorithm based method to distinguish computationally BC cell lines. The method is developed by coupling K neighbors Classifier (KNN) with Neighborhood Component Analysis (NCA) and NCA-KNN methods enables to identify BC subtypes without increasing model size as well additional parameters. Results: By incorporating FTIR imaging data, we show that using NCA-KNN method, the classification accuracies, specificities and sensitivities improve up to 97%, even at very low co-added scan (S_4). Moreover, a clear distinctive accuracy difference of our proposed method was obtained in comparison with other ML supervised models. Conclusion: For confirming our model results performance, the cross validation (k fold = 10) and receiver operation characteristics (ROC) curve were used and found in great agreement, suggest a potential diagnostic method for BC subtypes, even with small co-added scan < 8 at low spectral resolution (4 cm-1).pt_BR
dc.event.siglaSPECpt_BR
dc.identifier.citationFAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; NASCIMENTO, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M. Breast cancer subtypes diagnostic via high performance supervised machine learning. In: INTERNATIONAL CONFERENCE ON CLINICAL SPECTROSCOPY, 12th, June 19-23, 2022, Dublin, Ireland. <b>Abstract...</b> Disponível em: http://repositorio.ipen.br/handle/123456789/33938.
dc.identifier.orcid0000-0001-7404-9606pt_BR
dc.identifier.orcid0000-0002-0029-7313pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-0029-7313
dc.identifier.orcidhttps://orcid.org/0000-0001-7404-9606
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/33938
dc.local.eventoDublin, Irelandpt_BR
dc.rightsopenAccesspt_BR
dc.titleBreast cancer subtypes diagnostic via high performance supervised machine learningpt_BR
dc.typeResumo de eventos científicospt_BR
dspace.entity.typePublication
ipen.autorSAJID FAROOQ
ipen.autorSOFIA NASCIMENTO DOS SANTOS
ipen.autorEMERSON SOARES BERNARDES
ipen.autorDENISE MARIA ZEZELL
ipen.autorMOISES OLIVEIRA DOS SANTOS
ipen.autorMATHEUS DEL VALLE
ipen.codigoautor15722
ipen.codigoautor14464
ipen.codigoautor12099
ipen.codigoautor693
ipen.codigoautor8411
ipen.codigoautor15209
ipen.contributor.ipenauthorSAJID FAROOQ
ipen.contributor.ipenauthorSOFIA NASCIMENTO DOS SANTOS
ipen.contributor.ipenauthorEMERSON SOARES BERNARDES
ipen.contributor.ipenauthorDENISE MARIA ZEZELL
ipen.contributor.ipenauthorMOISES OLIVEIRA DOS SANTOS
ipen.contributor.ipenauthorMATHEUS DEL VALLE
ipen.date.recebimento23-03
ipen.event.datapadronizada2022pt_BR
ipen.identifier.ipendoc29572pt_BR
ipen.notas.internasAbstractpt_BR
ipen.type.genreResumo
relation.isAuthorOfPublication60d3fba4-40e1-482c-9eda-4530bc63fecb
relation.isAuthorOfPublicationab78881a-78eb-42be-a463-aaf80e70de3d
relation.isAuthorOfPublication8115c8bd-822c-4f5a-9f49-3c12570ed40a
relation.isAuthorOfPublicationa565f8ad-3432-4891-98c0-a587f497db21
relation.isAuthorOfPublication1660cd3d-a7bb-40e2-9724-77f28d5c866a
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.atividadeNASCIMENTO, SOFIA:14464:110:Npt_BR
sigepi.autor.atividadeSANTOS, MOISES O. dos:8411:920: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:
29572.pdf
Tamanho:
365.69 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 - Resumos