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Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis
dc.contributor.author | Medina Delgado, Byron | |
dc.contributor.author | Soto Vergel, Angelo Joseph | |
dc.contributor.author | PALACIOS ALVARADO, WLAMYR | |
dc.date.accessioned | 2021-11-08T21:59:47Z | |
dc.date.available | 2021-11-08T21:59:47Z | |
dc.date.issued | 2021-06-09 | |
dc.identifier.uri | http://repositorio.ufps.edu.co/handle/ufps/781 | |
dc.description.abstract | This article evaluates autoregressive modeling as a feature extraction method in a database of chromatographic signals from urine samples for non-invasive diagnostic support of prostate cancer in response to the research question: Can chromatographic signals from urine be characterized and used as a non-invasive method for cancer diagnosis? For this purpose, a database of 18 patients, 9 diagnosed with prostate cancer and 9 control patients, is consolidated, statistical methods are implemented to generate autoregressive coefficients from the data signals, and finally, the principal component analysis technique is applied for cross-class classification. As a result, a correct classification was obtained in the total number of samples validating the autoregressive modelling as a feature extraction method in contrast to the conventional methodology usually followed in chromatographic signal processing. | eng |
dc.format.extent | 06 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Journal of Physics: Conference Series | spa |
dc.relation.ispartof | Journal of Physics: Conference Series | |
dc.rights | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | eng |
dc.source | https://iopscience.iop.org/article/10.1088/1742-6596/1938/1/012011/meta | spa |
dc.title | Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis | eng |
dc.type | Artículo de revista | spa |
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dc.identifier.doi | https://doi.org/10.1088/1742-6596/1938/1/012011 | |
dc.publisher.place | Reino Unido | spa |
dc.relation.citationedition | Vol.1938 No.1.(2021) | spa |
dc.relation.citationendpage | 6 | spa |
dc.relation.citationissue | 1(2021) | spa |
dc.relation.citationstartpage | 1 | spa |
dc.relation.citationvolume | 1938 | spa |
dc.relation.cites | Soto-Vergel, A. J., Medina-Delgado, B., & Palacios-Alvarado, W. (2021, May). Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis. In Journal of Physics: Conference Series (Vol. 1938, No. 1, p. 012011). IOP Publishing. | |
dc.relation.ispartofjournal | Journal of Physics: Conference Series | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.creativecommons | Atribución 4.0 Internacional (CC BY 4.0) | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |