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Advancing Post-Genome Data and System Integration Through Machine Learning

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web....

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Detalles Bibliográficos
Autor principal: Azuaje, Francisco
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447240/
https://www.ncbi.nlm.nih.gov/pubmed/18628880
http://dx.doi.org/10.1002/cfg.129
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author Azuaje, Francisco
author_facet Azuaje, Francisco
author_sort Azuaje, Francisco
collection PubMed
description Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources.
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spelling pubmed-24472402008-07-14 Advancing Post-Genome Data and System Integration Through Machine Learning Azuaje, Francisco Comp Funct Genomics Research Article Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources. Hindawi Publishing Corporation 2002-02 /pmc/articles/PMC2447240/ /pubmed/18628880 http://dx.doi.org/10.1002/cfg.129 Text en Copyright © 2002 Hindawi Publishing Corporation. http://creativecommons.org/licenses/by/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Azuaje, Francisco
Advancing Post-Genome Data and System Integration Through Machine Learning
title Advancing Post-Genome Data and System Integration Through Machine Learning
title_full Advancing Post-Genome Data and System Integration Through Machine Learning
title_fullStr Advancing Post-Genome Data and System Integration Through Machine Learning
title_full_unstemmed Advancing Post-Genome Data and System Integration Through Machine Learning
title_short Advancing Post-Genome Data and System Integration Through Machine Learning
title_sort advancing post-genome data and system integration through machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447240/
https://www.ncbi.nlm.nih.gov/pubmed/18628880
http://dx.doi.org/10.1002/cfg.129
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