{"id":104311,"date":"2018-03-11T10:27:12","date_gmt":"2018-03-11T10:27:12","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/optimizacion-de-procesos-de-adquisicion-de-conocimiento-en-biologa%c2%ada-computacional\/"},"modified":"2018-03-11T10:27:12","modified_gmt":"2018-03-11T10:27:12","slug":"optimizacion-de-procesos-de-adquisicion-de-conocimiento-en-biologa%c2%ada-computacional","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/oncologia-clinica\/optimizacion-de-procesos-de-adquisicion-de-conocimiento-en-biologa%c2%ada-computacional\/","title":{"rendered":"Optimizaci\u00f3n de procesos de adquisici\u00f3n de conocimiento en biolog\u00eda computacional"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Santiago Gonz\u00e1lez Tortosa <\/strong><\/h2>\n<p>Tradicionalmente, los datos cl\u00ednicos han sido la \u00fanica fuente de informaci\u00f3n para el diagn\u00f3stico de enfermedades. Hoy en d\u00eda, existen otros tipos de informaci\u00f3n, como microarrays de adn, que permiten mejorar el diagn\u00f3stico y pron\u00f3stico en muchas enfermedades. Esta tesis propone un nuevo enfoque, denominado clidapa, para combinar eficientemente ambas fuentes de informaci\u00f3n (datos cl\u00ednicos y gen\u00e9ticos), de forma que se mejoren las estimaciones. Para ello, en primer lugar, los pacientes se segmentan utilizando una representaci\u00f3n en \u00e1rbol a trav\u00e9s de sus datos cl\u00ednicos (\u00e1rbol cl\u00ednico). Por tanto, se identifican distintas  agrupaciones de pacientes seg\u00fan comportamientos similares. A continuaci\u00f3n, se analiza cada agrupaci\u00f3n  independientemente con la informaci\u00f3n gen\u00e9tica asociada, mediante t\u00e9cnicas de miner\u00eda de datos. Para demostrar su validez, el m\u00e9todo se aplica a distintos conjuntos de datos reales (sobre c\u00e1ncer de mama y de cerebro). La validaci\u00f3n de los resultados se basa en dos m\u00e9todos de validaci\u00f3n, interna y externa, utilizando para ello el centro de supercomputaci\u00f3n y visualizaci\u00f3n de Madrid (cesvima), en donde se ejecutaron los tres enfoques paralelizados del algoritmo. Los resultados obtenidos se comparan con distintos estudios de la literatura, as\u00ed como con las t\u00e9cnicas de an\u00e1lisis tradicionales, demostrando una mejora significativa en los resultados existentes.  traditionally, clinical data have been the only source of information for disease diagnosis. Today, there are other types of information such as dna microarrays, which are taken into account to improve diagnosis and prognosis of many diseases. This thesis proposes a new approach, called clidapa, to efficiently combine both sources of information (clinical and genetic data), in order to further improve estimations. In this approach, patients are firstly segmented using a tree representation through their clinical data (clinical tree). Therefore, different groups of patients are identified according to similar behavior. Then each individual group is studied with data mining techniques, using the genetic information. To demonstrate its validity, the method is applied to different real data sets (breast and brain cancer). The validation of the results is based on two methods of validation, internal and external, using the supercomputing and visualization centre of Madrid (cesvima), where the three approaches of the algorithm were implemented in parallel. The results are compared with other literature studies, as well as traditional analysis techniques, demonstrating a significant improvement over existing results.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Optimizaci\u00f3n de procesos de adquisici\u00f3n de conocimiento en biolog\u00eda computacional<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Optimizaci\u00f3n de procesos de adquisici\u00f3n de conocimiento en biolog\u00eda computacional <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Santiago Gonz\u00e1lez Tortosa <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Polit\u00e9cnica de Madrid<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 18\/10\/2010<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>V\u00edctor Robles Forcada<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: ernestina Menasalvas ruiz <\/li>\n<li>cristobal Belda iniesta (vocal)<\/li>\n<li>endika Bengoechea castro (vocal)<\/li>\n<li>Luis Pastor p\u00e9rez (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Santiago Gonz\u00e1lez Tortosa Tradicionalmente, los datos cl\u00ednicos han sido la \u00fanica fuente de informaci\u00f3n para el diagn\u00f3stico [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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