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The age-phenome database

Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retri...

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Detalles Bibliográficos
Autores principales: Geifman, Nophar, Rubin, Eitan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3581109/
https://www.ncbi.nlm.nih.gov/pubmed/23984222
http://dx.doi.org/10.1186/2193-1801-1-4
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author Geifman, Nophar
Rubin, Eitan
author_facet Geifman, Nophar
Rubin, Eitan
author_sort Geifman, Nophar
collection PubMed
description Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retrieval of information concerning age-related phenotypic patterns. Here, we report that data derived from over 1.5 million human-related PubMed abstracts have been added to APK. Using a text-mining pipeline, 35,683 entries which describe relationships between age and phenotype (such as disease) have been introduced into the database. Comparing the results to those obtained by a human reader reveals that the overall accuracy of these entries is estimated to exceed 80%. The usefulness of these data for obtaining new insight regarding age-disease relationships is demonstrated using clustering analysis, which is shown to capture obvious, as well as potentially interesting relationships between diseases. In addition, a new tool for browsing and searching the APK database is presented. We thus present a unique resource and a new framework for studying age-disease relationships and other phenotypic processes.
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spelling pubmed-35811092013-08-27 The age-phenome database Geifman, Nophar Rubin, Eitan Springerplus Research Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retrieval of information concerning age-related phenotypic patterns. Here, we report that data derived from over 1.5 million human-related PubMed abstracts have been added to APK. Using a text-mining pipeline, 35,683 entries which describe relationships between age and phenotype (such as disease) have been introduced into the database. Comparing the results to those obtained by a human reader reveals that the overall accuracy of these entries is estimated to exceed 80%. The usefulness of these data for obtaining new insight regarding age-disease relationships is demonstrated using clustering analysis, which is shown to capture obvious, as well as potentially interesting relationships between diseases. In addition, a new tool for browsing and searching the APK database is presented. We thus present a unique resource and a new framework for studying age-disease relationships and other phenotypic processes. Springer 2012-04-23 /pmc/articles/PMC3581109/ /pubmed/23984222 http://dx.doi.org/10.1186/2193-1801-1-4 Text en Copyright ©2012 Geifman and Rubin; licensee Springer. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Geifman, Nophar
Rubin, Eitan
The age-phenome database
title The age-phenome database
title_full The age-phenome database
title_fullStr The age-phenome database
title_full_unstemmed The age-phenome database
title_short The age-phenome database
title_sort age-phenome database
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3581109/
https://www.ncbi.nlm.nih.gov/pubmed/23984222
http://dx.doi.org/10.1186/2193-1801-1-4
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