Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii
ilustraciones, gráficas, tablas
- Autores:
-
Aguilar Gonzalez, Karen Jhovana
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80437
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Acinetobacter baumannii
Bacterias
Farmacorresistencia Microbiana
Bacteria
Drug Resistance, Microbial
Artificial intelligence
Inteligencia artificial
Acinetobacter baumannii
Aprendizaje de máquina
Predicción fenotípica de resistencia
Concentración mínima inhibitoria
Regresión lasso
Random forest
Gradient boosting
- Rights
- openAccess
- License
- Atribución-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| dc.title.translated.eng.fl_str_mv |
Prediction of the resistance profile from the genome sequences of colombian isolates of Acinetobacter baumannii |
| title |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| spellingShingle |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Acinetobacter baumannii Bacterias Farmacorresistencia Microbiana Bacteria Drug Resistance, Microbial Artificial intelligence Inteligencia artificial Acinetobacter baumannii Aprendizaje de máquina Predicción fenotípica de resistencia Concentración mínima inhibitoria Regresión lasso Random forest Gradient boosting |
| title_short |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| title_full |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| title_fullStr |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| title_full_unstemmed |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| title_sort |
Predicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumannii |
| dc.creator.fl_str_mv |
Aguilar Gonzalez, Karen Jhovana |
| dc.contributor.advisor.none.fl_str_mv |
Barreto Hernández, Emiliano Reguero Reza, María Teresa Jesús |
| dc.contributor.author.none.fl_str_mv |
Aguilar Gonzalez, Karen Jhovana |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Epidemiologia Molecular Grupo de Bioinformatica |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Acinetobacter baumannii Bacterias Farmacorresistencia Microbiana Bacteria Drug Resistance, Microbial Artificial intelligence Inteligencia artificial Acinetobacter baumannii Aprendizaje de máquina Predicción fenotípica de resistencia Concentración mínima inhibitoria Regresión lasso Random forest Gradient boosting |
| dc.subject.decs.none.fl_str_mv |
Acinetobacter baumannii |
| dc.subject.decs.spa.fl_str_mv |
Bacterias Farmacorresistencia Microbiana |
| dc.subject.decs.eng.fl_str_mv |
Bacteria Drug Resistance, Microbial |
| dc.subject.lemb.eng.fl_str_mv |
Artificial intelligence |
| dc.subject.lemb.spa.fl_str_mv |
Inteligencia artificial |
| dc.subject.proposal.other.fl_str_mv |
Acinetobacter baumannii |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje de máquina Predicción fenotípica de resistencia Concentración mínima inhibitoria Regresión lasso |
| dc.subject.proposal.eng.fl_str_mv |
Random forest Gradient boosting |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-10-07T22:16:47Z |
| dc.date.available.none.fl_str_mv |
2021-10-07T22:16:47Z |
| dc.date.issued.none.fl_str_mv |
2021-04 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/80437 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/80437 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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He, T., Wang, R., Liu, D., Walsh, T. R., Zhang, R., Lv, Y., Ke, Y., Ji, Q., Wei, R., Liu, Z., Shen, Y., Wang, G., Sun, L., Lei, L., Lv, Z., Li, Y., Pang, M., Wang, L., Sun, Q., … Wang, Y. (2019). Emergence of plasmid-mediated high-level tigecycline resistance genes in animals and humans. Nature Microbiology, 4(9), 1450-1456. https://doi.org/10.1038/s41564-019-0445-2 Health, C. for D. and R. (2020). Antimicrobial Susceptibility Test (AST) Systems—Class II Special Controls Guidance for Industry and FDA. FDA. https://www.fda.gov/medical-devices/guidance-documents-medical-devices-and- radiation-emitting-products/antimicrobial-susceptibility-test-ast-systems-class-iispecial-controls-guidance-industry-and-fda Her, H.-L., & Wu, Y.-W. (2018). A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains. Bioinformatics, 34(13), i89-i95. https://doi.org/10.1093/bioinformatics/bty276 Hicks, A. 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Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. Journal of Antimicrobial Chemotherapy, 68(4), 771-777. https://doi.org/10.1093/jac/dks496 Zhang, C., Ju, Y., Tang, N., Li, Y., Zhang, G., Song, Y., Fang, H., Yang, L., & Feng, J. (2019). Systematic analysis of supervised machine learning as an effective approach to predicate β-lactam resistance phenotype in Streptococcus pneumoniae. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbz056 Zhang, Yanpeng, Li, Z., He, X., Ding, F., Wu, W., Luo, Y., Fan, B., & Cao, H. (2018). Overproduction of efflux pumps caused reduced susceptibility to carbapenem under consecutive imipenem-selected stress in Acinetobacter baumannii. Infection and Drug Resistance, 11, 457-467. https://doi.org/10.2147/IDR.S151423 Zheng, W., Sun, W., & Simeonov, A. (2019). Drug repurposing screens and synergistic drug-combinations for infectious diseases. British Journal of Pharmacology, 181- 191. https://doi.org/10.1111/bph.13895@10.1111/(ISSN)1476-5381.BJP-BJCPOpen-Access-Collection |
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Bogotá - Ciencias - Maestría en Ciencias - Microbiología |
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Observatorio Astronómico Nacional |
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Facultad de Ciencias |
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Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Barreto Hernández, Emiliano67ae3f3c229b893d7e0d55415b69074a600Reguero Reza, María Teresa Jesús9ba54bc0326cdc978d84a9a9130f2d0cAguilar Gonzalez, Karen Jhovanae60e5d7085f1d8f7c76674b9031c4c87Grupo de Epidemiologia MolecularGrupo de Bioinformatica2021-10-07T22:16:47Z2021-10-07T22:16:47Z2021-04https://repositorio.unal.edu.co/handle/unal/80437Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa 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).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.Incluye anexosMaestríaMagíster en Ciencias - MicrobiologíaBiología molecular de agentes infecciososxiii, 155 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - MicrobiologíaObservatorio Astronómico NacionalFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresAcinetobacter baumanniiBacteriasFarmacorresistencia MicrobianaBacteriaDrug Resistance, MicrobialArtificial intelligenceInteligencia artificialAcinetobacter baumanniiAprendizaje de máquinaPredicción fenotípica de resistenciaConcentración mínima inhibitoriaRegresión lassoRandom forestGradient boostingPredicción del perfil de resistencia a partir de las secuencias del genoma de aislamientos colombianos de Acinetobacter baumanniiPrediction of the resistance profile from the genome sequences of colombian isolates of Acinetobacter baumanniiTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAbdi, S. 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