Kernel-based manifold visualization of GPCR sequences
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Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099.1/12663
Tipus de documentProjecte Final de Màster Oficial
Data2011-06-22
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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
TitulacióMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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Ivon cardenas.pdf | 10,56Mb | Visualitza/Obre |