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Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii
dc.rights.license | Atribución-SinDerivadas 4.0 Internacional |
dc.contributor.advisor | Barreto Hernández, Emiliano |
dc.contributor.advisor | Reguero Reza, María Teresa Jesús |
dc.contributor.author | Aguilar Gonzalez, Karen Jhovana |
dc.date.accessioned | 2021-10-07T22:16:47Z |
dc.date.available | 2021-10-07T22:16:47Z |
dc.date.issued | 2021-04 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/80437 |
dc.description | ilustraciones, gráficas, tablas |
dc.description.abstract | La creciente resistencia a los antibióticos y las pocas alternativas terapéuticas disponibles vuelven una urgencia la necesidad de optimizar los diagnósticos actuales que nos permitan prescripciones más rápidas y efectivas. Últimamente uno de los enfoques para predecir resistencia a partir de los datos de secuenciación de genoma consta en aplicar modelos basados en aprendizaje de máquina, los cuales han ido tomando credibilidad debido a la capacidad de realizar predicciones precisas. Además, gracias al creciente conocimiento acerca de mecanismos de resistencia asociados a A. baumannii, este patógeno nos brinda una alternativa para desarrollar estos modelos. En este trabajo se utilizaron 343 genomas, 76 colombianos del Instituto Nacional de Salud y 267 recolectados de la base de datos Biosample NCBI, para la obtención de modelos basados en aprendizaje de máquina empleando regresión lasso, random forest y gradient boosting, para predecir la concentración mínima inhibitoria de 10 antibióticos. Random forest fue el algoritmo que mostró los mejores resultados, logrando una precisión promedio dentro de +/- una dilución doble de 91% (I.C 95, 85- 97), una tasa de very major error y major error de 1,71% y 0,7%, respectivamente. Como datos de entrada para los modelos se utilizaron genes de resistencia, los cuales fueron identificados utilizando el software Resistance Gene Identifier. Estos resultados demuestran que la predicción de la susceptibilidad de A. baumannii a los antibióticos, basada en la secuencia del genoma son prometedoras como posibles herramienta de diagnóstico en la clínica. (Texto tomado de la fuente). |
dc.description.abstract | The increasing resistance to antibiotics and the few therapeutic choices available turns into an urgency the need to optimize the current diagnoses that allow faster and more efficient prescriptions. Lately, an approach to predict resistance from genome sequencing data applies machine learning models, which has taken credibility due to its capability to make reliable predictions. Further, thanks to the increasing knowledge about resistance mechanisms associated with A. baumannii, there is enough accessible data for developing these models. We used 343 genomes, 76 from the Colombian National Institute of Health, and 267 gathered from the Biosample NCBI database. We created models based on machine learning using lasso regression, random forest, and gradient boosting, to predict the minimum inhibitory concentration of 10 antibiotics. Random forest was the algorithm that show is better results, achieving an average accuracy within +/- a double dilution of 91% (I.C 95, 85- 97), a very major error, and a major error rate of 1.71% and 0.7% respectively. We employ known resistance genes as the model input, which were identified using the Resistance Gene Identifier software. These results show that the A. baumannii antibiotics susceptibility prediction, based on genome sequence, is promising as a possible diagnostic tool in the clinic. |
dc.format.extent | xiii, 155 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | spa |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
dc.title | Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
dc.type | Trabajo de grado - Maestría |
dc.type.driver | info:eu-repo/semantics/masterThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Bogotá - Ciencias - Maestría en Ciencias - Microbiología |
dc.description.notes | Incluye anexos |
dc.contributor.researchgroup | Grupo de Epidemiologia Molecular |
dc.contributor.researchgroup | Grupo de Bioinformatica |
dc.description.degreelevel | Maestría |
dc.description.degreename | Magíster en Ciencias - Microbiología |
dc.description.researcharea | Biología molecular de agentes infecciosos |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.department | Observatorio Astronómico Nacional |
dc.publisher.faculty | Facultad de Ciencias |
dc.publisher.place | Bogotá, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.decs | Acinetobacter baumannii |
dc.subject.decs | Bacterias |
dc.subject.decs | Bacteria |
dc.subject.decs | Farmacorresistencia Microbiana |
dc.subject.decs | Drug Resistance, Microbial |
dc.subject.lemb | Artificial intelligence |
dc.subject.lemb | Inteligencia artificial |
dc.subject.proposal | Acinetobacter baumannii |
dc.subject.proposal | Aprendizaje de máquina |
dc.subject.proposal | Predicción fenotípica de resistencia |
dc.subject.proposal | Concentración mínima inhibitoria |
dc.subject.proposal | Regresión lasso |
dc.subject.proposal | Random forest |
dc.subject.proposal | Gradient boosting |
dc.title.translated | Prediction of the resistance profile from the genome sequences of colombian isolates of Acinetobacter baumannii |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TM |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
dcterms.audience.professionaldevelopment | Público general |
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