Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje
Este informe presenta la descripción del diseño y la obtención de un clasificador de péptidos antimicrobianos que permite realizar una preselección de cadenas peptídicas, de manera que se reduzca el número de experimentos necesarios para encontrar alguna con actividad antibacteriana. Se hace uso de...
- Autores:
-
Coba Cruz, Diego Fernando
- Tipo de recurso:
- http://purl.org/coar/version/c_b1a7d7d4d402bcce
- Fecha de publicación:
- 2016
- Institución:
- Universidad Industrial de Santander
- Repositorio:
- Repositorio UIS
- Idioma:
- spa
- OAI Identifier:
- oai:noesis.uis.edu.co:20.500.14071/35074
- Palabra clave:
- Clasificador
Péptido
Máquinas De Soporte Vectorial
Validación Cruzada
Knn
Potencial De Interacción Ion Electrón
Señal Discreta
Representación En Frecuencia.
This paper presents the design and implementation of an antibacterial peptides classifier that allow the pre-selection of those sequences that possess antibacterial activity in order to reduce the quantity of experiments that should be performed to find a successful antibacterial peptide. Supervised learning models were used in order to teach the system how to discriminate a peptide with Support vector machines with linear and radial basis functions
and k-nearest neighbors with uniform and distance dependent weights were implemented. Vector and frequency spectrum mathematical representations where used both normalized and no normalized forms. For each combination of learning model
learning model parameters and mathematical representation
the estimated assessment was determined through Nested K Fold Cross Validation process to finally take the one with the best performance which was obtained using K Fold Cross Validation process. Finally
a comparison between the different learning models and representations was made as conclusions of this work. The software created for this purpose is made by modules that allow the development of each one of the process stages. The final result is composed of the obtained classifiers
their estimated assessment metrics file and the system through which were gotten. 3
- Rights
- License
- Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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dc.title.none.fl_str_mv |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
dc.title.english.none.fl_str_mv |
Classifier, Peptide, Support Vector Machine, K Fold Cross Validation, Knn, Electron Ion Interaction Potential, Digital Signal, Frequency Spectrum. |
title |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
spellingShingle |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje Clasificador Péptido Máquinas De Soporte Vectorial Validación Cruzada Knn Potencial De Interacción Ion Electrón Señal Discreta Representación En Frecuencia. This paper presents the design and implementation of an antibacterial peptides classifier that allow the pre-selection of those sequences that possess antibacterial activity in order to reduce the quantity of experiments that should be performed to find a successful antibacterial peptide. Supervised learning models were used in order to teach the system how to discriminate a peptide with Support vector machines with linear and radial basis functions and k-nearest neighbors with uniform and distance dependent weights were implemented. Vector and frequency spectrum mathematical representations where used both normalized and no normalized forms. For each combination of learning model learning model parameters and mathematical representation the estimated assessment was determined through Nested K Fold Cross Validation process to finally take the one with the best performance which was obtained using K Fold Cross Validation process. Finally a comparison between the different learning models and representations was made as conclusions of this work. The software created for this purpose is made by modules that allow the development of each one of the process stages. The final result is composed of the obtained classifiers their estimated assessment metrics file and the system through which were gotten. 3 |
title_short |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
title_full |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
title_fullStr |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
title_full_unstemmed |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
title_sort |
Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizaje |
dc.creator.fl_str_mv |
Coba Cruz, Diego Fernando |
dc.contributor.advisor.none.fl_str_mv |
Sierra Bueno, Daniel Alfonso Rondon Villarreal, Nydia Paola |
dc.contributor.author.none.fl_str_mv |
Coba Cruz, Diego Fernando |
dc.subject.none.fl_str_mv |
Clasificador Péptido Máquinas De Soporte Vectorial Validación Cruzada Knn Potencial De Interacción Ion Electrón Señal Discreta Representación En Frecuencia. |
topic |
Clasificador Péptido Máquinas De Soporte Vectorial Validación Cruzada Knn Potencial De Interacción Ion Electrón Señal Discreta Representación En Frecuencia. This paper presents the design and implementation of an antibacterial peptides classifier that allow the pre-selection of those sequences that possess antibacterial activity in order to reduce the quantity of experiments that should be performed to find a successful antibacterial peptide. Supervised learning models were used in order to teach the system how to discriminate a peptide with Support vector machines with linear and radial basis functions and k-nearest neighbors with uniform and distance dependent weights were implemented. Vector and frequency spectrum mathematical representations where used both normalized and no normalized forms. For each combination of learning model learning model parameters and mathematical representation the estimated assessment was determined through Nested K Fold Cross Validation process to finally take the one with the best performance which was obtained using K Fold Cross Validation process. Finally a comparison between the different learning models and representations was made as conclusions of this work. The software created for this purpose is made by modules that allow the development of each one of the process stages. The final result is composed of the obtained classifiers their estimated assessment metrics file and the system through which were gotten. 3 |
dc.subject.keyword.none.fl_str_mv |
This paper presents the design and implementation of an antibacterial peptides classifier that allow the pre-selection of those sequences that possess antibacterial activity in order to reduce the quantity of experiments that should be performed to find a successful antibacterial peptide. Supervised learning models were used in order to teach the system how to discriminate a peptide with Support vector machines with linear and radial basis functions and k-nearest neighbors with uniform and distance dependent weights were implemented. Vector and frequency spectrum mathematical representations where used both normalized and no normalized forms. For each combination of learning model learning model parameters and mathematical representation the estimated assessment was determined through Nested K Fold Cross Validation process to finally take the one with the best performance which was obtained using K Fold Cross Validation process. Finally a comparison between the different learning models and representations was made as conclusions of this work. The software created for this purpose is made by modules that allow the development of each one of the process stages. The final result is composed of the obtained classifiers their estimated assessment metrics file and the system through which were gotten. 3 |
description |
Este informe presenta la descripción del diseño y la obtención de un clasificador de péptidos antimicrobianos que permite realizar una preselección de cadenas peptídicas, de manera que se reduzca el número de experimentos necesarios para encontrar alguna con actividad antibacteriana. Se hace uso de métodos de aprendizaje supervisado para que el sistema aprenda a diferenciar una cadena con la propiedad deseada de una que no la posee. Se implementan los métodos Máquinas de Soporte Vectorial con kernel tanto lineal como de base radial y el algoritmo de Los K Vecinos más cercanos con pesos tanto uniformes como dependientes de la distancia. Se emplean la representación vectorial y la representación en frecuencia obtenida a partir de la representación como señal discreta, cada una tanto normalizada como sin normalizar. Para cada combinación de método de aprendizaje, parámetros libres y representación de datos, se valida el rendimiento mediante el método de Validación Cruzada anidada para finalmente tomar el clasificador con el mejor resultado. Este clasificador final es obtenido mediante el método de Validación Cruzada. El software creado para tal fin se compone de módulos que permiten realizar cada una de las etapas. El resultado final consta de los clasificadores obtenidos, su estimación de rendimiento y el sistema con el que se obtienen. 1 |
publishDate |
2016 |
dc.date.available.none.fl_str_mv |
2016 2024-03-03T22:44:23Z |
dc.date.created.none.fl_str_mv |
2016 |
dc.date.issued.none.fl_str_mv |
2016 |
dc.date.accessioned.none.fl_str_mv |
2024-03-03T22:44:23Z |
dc.type.local.none.fl_str_mv |
Tesis/Trabajo de grado - Monografía - Pregrado |
dc.type.hasversion.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
format |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
dc.identifier.uri.none.fl_str_mv |
https://noesis.uis.edu.co/handle/20.500.14071/35074 |
dc.identifier.instname.none.fl_str_mv |
Universidad Industrial de Santander |
dc.identifier.reponame.none.fl_str_mv |
Universidad Industrial de Santander |
dc.identifier.repourl.none.fl_str_mv |
https://noesis.uis.edu.co |
url |
https://noesis.uis.edu.co/handle/20.500.14071/35074 https://noesis.uis.edu.co |
identifier_str_mv |
Universidad Industrial de Santander |
dc.language.iso.none.fl_str_mv |
spa |
language |
spa |
dc.rights.none.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.none.fl_str_mv |
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) |
dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0 |
dc.rights.creativecommons.none.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
rights_invalid_str_mv |
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by-nc/4.0 Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2 |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidad Industrial de Santander |
dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingenierías Fisicomecánicas |
dc.publisher.program.none.fl_str_mv |
Ingeniería Electrónica |
dc.publisher.school.none.fl_str_mv |
Escuela de Ingenierías Eléctrica, Electrónica y Telecomunicaciones |
publisher.none.fl_str_mv |
Universidad Industrial de Santander |
institution |
Universidad Industrial de Santander |
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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by-nc/4.0Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Sierra Bueno, Daniel AlfonsoRondon Villarreal, Nydia PaolaCoba Cruz, Diego Fernando2024-03-03T22:44:23Z20162024-03-03T22:44:23Z20162016https://noesis.uis.edu.co/handle/20.500.14071/35074Universidad Industrial de SantanderUniversidad Industrial de Santanderhttps://noesis.uis.edu.coEste informe presenta la descripción del diseño y la obtención de un clasificador de péptidos antimicrobianos que permite realizar una preselección de cadenas peptídicas, de manera que se reduzca el número de experimentos necesarios para encontrar alguna con actividad antibacteriana. Se hace uso de métodos de aprendizaje supervisado para que el sistema aprenda a diferenciar una cadena con la propiedad deseada de una que no la posee. Se implementan los métodos Máquinas de Soporte Vectorial con kernel tanto lineal como de base radial y el algoritmo de Los K Vecinos más cercanos con pesos tanto uniformes como dependientes de la distancia. Se emplean la representación vectorial y la representación en frecuencia obtenida a partir de la representación como señal discreta, cada una tanto normalizada como sin normalizar. Para cada combinación de método de aprendizaje, parámetros libres y representación de datos, se valida el rendimiento mediante el método de Validación Cruzada anidada para finalmente tomar el clasificador con el mejor resultado. Este clasificador final es obtenido mediante el método de Validación Cruzada. El software creado para tal fin se compone de módulos que permiten realizar cada una de las etapas. El resultado final consta de los clasificadores obtenidos, su estimación de rendimiento y el sistema con el que se obtienen. 1PregradoIngeniero ElectrónicoDesign and creation of an antibacterial peptide classifier using digital signal processing technics and supervised learning models.3application/pdfspaUniversidad Industrial de SantanderFacultad de Ingenierías FisicomecánicasIngeniería ElectrónicaEscuela de Ingenierías Eléctrica, Electrónica y TelecomunicacionesClasificadorPéptidoMáquinas De Soporte VectorialValidación CruzadaKnnPotencial De Interacción Ion ElectrónSeñal DiscretaRepresentación En Frecuencia.This paper presents the design and implementation of an antibacterial peptides classifier that allow the pre-selection of those sequences that possess antibacterial activity in order to reduce the quantity of experiments that should be performed to find a successful antibacterial peptide. Supervised learning models were used in order to teach the system how to discriminate a peptide with Support vector machines with linear and radial basis functionsand k-nearest neighbors with uniform and distance dependent weights were implemented. Vector and frequency spectrum mathematical representations where used both normalized and no normalized forms. For each combination of learning modellearning model parameters and mathematical representationthe estimated assessment was determined through Nested K Fold Cross Validation process to finally take the one with the best performance which was obtained using K Fold Cross Validation process. Finallya comparison between the different learning models and representations was made as conclusions of this work. The software created for this purpose is made by modules that allow the development of each one of the process stages. The final result is composed of the obtained classifierstheir estimated assessment metrics file and the system through which were gotten. 3Diseño y creación de un clasificador de péptidos antibacterianos utilizando técnicas de procesamiento digital de señales y algoritmos de aprendizajeClassifier, Peptide, Support Vector Machine, K Fold Cross Validation, Knn, Electron Ion Interaction Potential, Digital Signal, Frequency Spectrum.Tesis/Trabajo de grado - Monografía - Pregradohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_b1a7d7d4d402bcceORIGINALCarta de autorización.pdfapplication/pdf250414https://noesis.uis.edu.co/bitstreams/64f6e5c3-b201-495a-b736-bd70076e6413/downloadab4ae8c3ac0660bef2d4d18b7b0647edMD51Documento.pdfapplication/pdf4229364https://noesis.uis.edu.co/bitstreams/7589ad9d-4dd2-4ae9-8dae-1570ac1970d3/download4ac6206c42c4ab737f85de09b05d8ea3MD52Nota de proyecto.pdfapplication/pdf207488https://noesis.uis.edu.co/bitstreams/c2fe7a8b-0b97-461b-890a-5d92ac40e3ca/download6c35fe264a3a538cd11fb584b25b0bc6MD5320.500.14071/35074oai:noesis.uis.edu.co:20.500.14071/350742024-03-03 17:44:23.321http://creativecommons.org/licenses/by-nc/4.0http://creativecommons.org/licenses/by/4.0/open.accesshttps://noesis.uis.edu.coDSpace at UISnoesis@uis.edu.co |