dc.contributor |
Universitat Politècnica de Catalunya. Departament de Física Aplicada |
dc.contributor |
Vellido Alcacena, Alfredo |
dc.contributor |
Giraldo, Jesús |
dc.contributor.author |
Cárdenas Domíınguez, Martha Ivón |
dc.date |
2011-06-22 |
dc.identifier.uri |
http://hdl.handle.net/2099.1/12663 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.rights |
Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts |
dc.subject |
Genomics |
dc.subject |
G-Protein Coupled Receptors (GPCRs) |
dc.subject |
Statistical machine learning |
dc.subject |
Genòmica |
dc.title |
Kernel-based manifold visualization of GPCR sequences |
dc.type |
info:eu-repo/semantics/masterThesis |
dc.description.abstract |
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 |