Abstract
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.
Original language | American English |
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Article number | 4 |
Pages (from-to) | 1-8 |
Number of pages | 8 |
Journal | SpringerPlus |
Volume | 1 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2012 |
Keywords
- Age
- Knowledgebase
- Phenotype
- Text-minig
All Science Journal Classification (ASJC) codes
- General