Kernel-based manifold visualization of GPCR sequences

G-Protein Coupled Receptors (GPCRs) are key players in cell- cell communication. They transduce a wide range of extracellular signals such as light, odors, hormones or neurotransmitters into ap- propriated cellular responses. These receptors regulate many cell functions and are encoded by the larges...

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Detalhes bibliográficos
Autor: Cárdenas Domíınguez, Martha Ivón|||0000-0001-7552-6021
Formato: tesis de maestría
Fecha de publicación:2011
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2099.1/12663
Acesso em linha:https://hdl.handle.net/2099.1/12663
Access Level:acceso abierto
Palavra-chave:Genomics
G-Protein Coupled Receptors (GPCRs)
Statistical machine learning
Genòmica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts
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spelling Kernel-based manifold visualization of GPCR sequencesCárdenas Domíınguez, Martha Ivón|||0000-0001-7552-6021GenomicsG-Protein Coupled Receptors (GPCRs)Statistical machine learningGenòmicaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes expertsG-Protein Coupled Receptors (GPCRs) are key players in cell- cell communication. They transduce a wide range of extracellular signals such as light, odors, hormones or neurotransmitters into ap- propriated cellular responses. These receptors regulate many cell functions and are encoded by the largest gene family in mammalian genomes, representing more than 3% of the human genes. GPCRs are the estimated target of approximately half of the medicines cur- rently in clinical use. Probabilistic modelling and specifically, machine learning prob- abilistic models have only recently begun to be applied to the anal- ysis of GPCR functioning, although their application is expected to generate new insights in this field. Statistical machine learning techniques are specially suited to deal with some of the common challenges of molecular modelling in proteins, and should be of spe- cial interest when the three dimensional structures of the proteins and receptors remain unknown at large. In this thesis, we describe a statistical machine learning model of the manifold learning family, adapted through kernelization to the analysis of protein sequence data. Experimental results show that it provides a differentiated visualization and grouping of GPCR subfamilies and that these groupings faithfully reflect the structure of GPCR phylogenetic trees. 3Universitat Politècnica de CatalunyaVellido Alcacena, AlfredoGiraldo, Jesús20112011-06-2220112011-07-15master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2099.1/12663reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099.1/126632026-05-27T15:37:01Z
dc.title.none.fl_str_mv Kernel-based manifold visualization of GPCR sequences
title Kernel-based manifold visualization of GPCR sequences
spellingShingle Kernel-based manifold visualization of GPCR sequences
Cárdenas Domíınguez, Martha Ivón|||0000-0001-7552-6021
Genomics
G-Protein Coupled Receptors (GPCRs)
Statistical machine learning
Genòmica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts
title_short Kernel-based manifold visualization of GPCR sequences
title_full Kernel-based manifold visualization of GPCR sequences
title_fullStr Kernel-based manifold visualization of GPCR sequences
title_full_unstemmed Kernel-based manifold visualization of GPCR sequences
title_sort Kernel-based manifold visualization of GPCR sequences
dc.creator.none.fl_str_mv Cárdenas Domíınguez, Martha Ivón|||0000-0001-7552-6021
author Cárdenas Domíınguez, Martha Ivón|||0000-0001-7552-6021
author_facet Cárdenas Domíınguez, Martha Ivón|||0000-0001-7552-6021
author_role author
dc.contributor.none.fl_str_mv Vellido Alcacena, Alfredo
Giraldo, Jesús
dc.subject.none.fl_str_mv Genomics
G-Protein Coupled Receptors (GPCRs)
Statistical machine learning
Genòmica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts
topic Genomics
G-Protein Coupled Receptors (GPCRs)
Statistical machine learning
Genòmica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts
description G-Protein Coupled Receptors (GPCRs) are key players in cell- cell communication. They transduce a wide range of extracellular signals such as light, odors, hormones or neurotransmitters into ap- propriated cellular responses. These receptors regulate many cell functions and are encoded by the largest gene family in mammalian genomes, representing more than 3% of the human genes. GPCRs are the estimated target of approximately half of the medicines cur- rently in clinical use. Probabilistic modelling and specifically, machine learning prob- abilistic models have only recently begun to be applied to the anal- ysis of GPCR functioning, although their application is expected to generate new insights in this field. Statistical machine learning techniques are specially suited to deal with some of the common challenges of molecular modelling in proteins, and should be of spe- cial interest when the three dimensional structures of the proteins and receptors remain unknown at large. In this thesis, we describe a statistical machine learning model of the manifold learning family, adapted through kernelization to the analysis of protein sequence data. Experimental results show that it provides a differentiated visualization and grouping of GPCR subfamilies and that these groupings faithfully reflect the structure of GPCR phylogenetic trees. 3
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-06-22
2011
2011-07-15
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099.1/12663
url https://hdl.handle.net/2099.1/12663
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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