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https://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/12102Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.advisor | Mendoza, Luis Enrique | - |
| dc.contributor.author | Villamizar Rozo., William | - |
| dc.date.accessioned | 2026-10-28T17:55:32Z | - |
| dc.date.available | 2012-09 | - |
| dc.date.available | 2026-10-28T17:55:32Z | - |
| dc.date.issued | 2012-09 | - |
| dc.identifier.uri | https://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/12102 | - |
| dc.description | En los últimos años, los datos metabolómicos provenientes de diferentes técnicas analíticas, como el caso del espectrómetro de resonancia magnética nuclear (HNMR), han tenido un nuevo campo de investigación permitiendo determinar metabolitos característicos en un determinado espectro. Con lo expuesto anteriormente; el presente capitulo plantea los motivos y objetivos, los cuales pretenden resaltar y proyectar, las variables relevantes en una serie de datos metabolómicos, específicamente en los aceites de oliva y avellana. | es_CO |
| dc.description.abstract | In recent years, metabolomic data from different analytical techniques, such as the case of the nuclear magnetic resonance spectrometer (HNMR), have had a new field of research allowing the determination of characteristic metabolites in a certain spectrum. With the previously exposed; This chapter presents the reasons and objectives, which are intended to highlight and project the relevant variables in a series of metabolomic data, specifically in olive and hazelnut oils. | es_CO |
| dc.description.tableofcontents | CAPITULO I ...................................................................................................... 13 1.INTRODUCCION ........................................................................................... 13 1.1 MOTIVACIÓN ....................................................................................... 13 1.2 OBJETIVO GENERAL ............................................................................ 15 1.3 OBJETIVOS ESPECIFICOS .................................................................. 15 CAPITULO II ..................................................................................................... 16 2. MARCO TEORICO ...................................................................................... 16 2.1 DATOS METABOLOMICOS .................................................................... 16 2.1.1. Análisis de puntajes (Targeted) ........................................................ 19 2.1.2. Huella Metabólica ............................................................................ 19 2.1.3. Perfil del Metabolito .......................................................................... 19 2. 2. Resonancia Magnética Nuclear Protónica (HNMR) ............................... 20 2.2.1 El espectrómetro de resonancia magnética nuclear .......................... 21 2.3 PREPROCESAMIENTO DE LOS DATOS .............................................. 22 2.3.1 Intervalos de igual amplitud o método de los cubos (Binning). .......... 22 2.3.2 Alineamiento de picos (peakalignment). ............................................ 25 2.3.3 Corrección de línea base ................................................................... 30 2.3.4 Normalización o Escalado. ................................................................ 32 .2.3.5 Suavizado (smoothing) .................................................................... 34 2.4. MÉTODOS DE CLASIFICACIÓN ........................................................... 36 2.4.1 METODOS MULTIVARIABLES DE CLASIFICACION ...................... 36 CAPITULO III………………………...…………………………………..…………..45 3. MATERIALES ............................................................................................... 45 CAPITULO IV………………………...…………………………………..…………..50 4. METODOLOGIA ........................................................................................... 49 4.1 Diagrama en bloques de la metodología ................................................ 49 4.1.1. Datos Metabolómicos ...................................................................... 49 4.1.2. Preprocesamiento de datos. ............................................................ 50 4.1.3. Selección del metabolito ................................................................. 55 4.1.4 Técnicas de Clasificación .................................................................. 56 5. RESULTADOS ............................................................................................. 57 5.1 Análisis data cruda (señal original) .......................................................... 57 5.2 Normalización y escalado min-máx. ....................................................... 59 5.3 Zona de interés ........................................................................................ 60 5.4 Corrección de línea base ......................................................................... 62 5.5 Alineamiento de picos ............................................................................. 63 | es_CO |
| dc.format.mimetype | application/pdf | es_CO |
| dc.language.iso | es | es_CO |
| dc.publisher | Universidad de Pamplona | es_CO |
| dc.subject | Resonancia magnética nuclear | es_CO |
| dc.subject | Metabolómicos | es_CO |
| dc.title | Implementación de técnicas de reconocimiento de patrones (least square support vector machines) en procesos de selección de parámetros característicos aplicados a sistemas metabolómicos | es_CO |
| dc.type | http://purl.org/coar/resource_type/c_bdcc | es_CO |
| dc.date.accepted | 2012-09 | - |
| dc.relation.references | [1] Oliver S.G, Winson MK, Kell D.B, et al. Systematic functional analysis of the yeast genome.TrendsBiotechnol 1998; 16:373–8. | es_CO |
| dc.relation.references | [2] VladimirShulaev, Metabolomics technology and bioinformatics. Briefings in Bioinformatics.2006; Vol. 7. No 2. 128 -139. | es_CO |
| dc.relation.references | [3] Viant MR, Rosenblum E.S, Tieerdema RS. NMR-based metabolomics: a powerful approach for characterizing theeffects of environmental stressors on organism health. Environ SciTechnol 2003; 37:4982–9. | es_CO |
| dc.relation.references | [4] Hong-Seok Son, Ki Myong Kim, Frans Van Den Berg,Geum-Sook Hwang, Won-Mok Park, Cherl-Ho Lee, and Young-ShickHong,J. 1H Nuclear Magnetic Resonance-Based Metabolomic Characterization of Wines by Grape Varieties and Production Areas.Agric. Food Chem. 2008, 56, 8007–8016 | es_CO |
| dc.relation.references | [5] D. F. Brougham, G. Ivanova, M. Gottschalk, D.M. Collins, A. J. Eustace, R. O’Connor, and J. Havel, Artificial Neural Networks for Classification in Metabolomic Studies ofWhole Cells Using 1H Nuclea rMagnetic Resonance. | es_CO |
| dc.relation.references | [6] Richard J. Gilbert, Helen E. Johnson, Michæl K. Winson, Jem J. Rowland, Royston Goodacre, Aileen R. Smith, Michæl A. Hall and Douglas B. Kell. Genetic Programming as an Analytical Tool for MetabolomeData;Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion. | es_CO |
| dc.relation.references | [7] Z. Ramadan, D. Jacobs, M. Grigorov, S. Kochhar. Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms. Elsevier, Talanta 68 (2006) 1683–1691 | es_CO |
| dc.relation.references | [8] Darwin on the origin of species by means of natural selection.Canadian Naturalist and Geologist5:100-120. | es_CO |
| dc.relation.references | [9] Vapnik, V. (1998b).The support vector method of function estimation. In J. A. K. Suykens, & J. Vandewalle, (Eds.), Nonlinear Modeling: Advanced Black-box Techniques. Boston: Kluwer Academic Publishers. | es_CO |
| dc.relation.references | [10] Jan Luts, Fabian Ojeda, Raf Van de Plasa,Bart De Moora, Sabine Van Huffela, Johan A.K. Suykensa , A tutorial on support vector machine-based methods for classification problems in chemometrics. Elsevier AnalyticaChimicaActa 665 (2010) 129–145 | es_CO |
| dc.relation.references | [11] Raamsdonk LM, Teusink B, Broadhurst D, et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 2001;19:45–50 | es_CO |
| dc.relation.references | [12] Catchpole GS, Beckmann M, Enot DP, et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. PNAS 2005;102:14458–62. | es_CO |
| dc.relation.references | [13] Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’:understanding the metabolic responses of living systems topathophysiological stimuli via multivariate statistical analysisof biological NMR spectroscopic data. Xenobiotica 1999; 29:1181–9. | es_CO |
| dc.relation.references | [14] Watkins SM, German J. B. Metabolomics and biochemical profiling in drug discovery and development.CurrOpinMolTher 2002;4:224–8. | es_CO |
| dc.relation.references | [15] Watkins SM, Reifsnyder PR, Pan HJ, et al. Lipid metabolome-wide effects of the PPAR gamma agonistrosiglitazone. J Lipid Res 2002;43:1809–17. | es_CO |
| dc.relation.references | [16] Jennifer L. Spratlin,NatalieJ. Serkova, and S. Gail Eckhardt, Clinical Applications of Metabolomics in Oncology: AReview.ClinCancerRes2009;15:431-440. | es_CO |
| dc.relation.references | [17]Fiehn O, Kopka J, Trethewey RN, et al. Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Anal Chem 2000; 72: 3573–80. | es_CO |
| dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | es_CO |
| dc.type.coarversion | http://purl.org/coar/version/c_970fb48d4fbd8a85 | es_CO |
| Aparece en las colecciones: | Maestría en Controles Industriales | |
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| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
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