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The age-phenome database.

Nophar Geifman1, Eitan Rubin

  • 1Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences and The National Institute for Biotechnology in the Negev, Ben Gurion University, Beersheva 84105, Israel.

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|August 29, 2013
PubMed
Summary
This summary is machine-generated.

This study enhances the Age-Phenome Knowledge-base (APK) with over 1.5 million PubMed abstracts, improving the search for age-disease relationships. The updated APK offers a valuable resource for understanding age-related phenotypes.

Keywords:
AgeKnowledgebasePhenotypeText-minig

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Gerontology

Background:

  • Biomedical literature contains extensive age-disease data, but it's difficult to access and search.
  • Existing knowledge organization hinders the exploration of age-phenotype relationships.

Purpose of the Study:

  • To expand the Age-Phenome Knowledge-base (APK) with a large dataset of human-related PubMed abstracts.
  • To create a searchable resource for age-related phenotypic patterns, specifically age-disease relationships.
  • To develop a framework for studying age-disease associations and other phenotypic processes.

Main Methods:

  • A text-mining pipeline was employed to extract 35,683 entries detailing age-phenotype relationships from over 1.5 million PubMed abstracts.
  • The accuracy of the extracted data was validated against human readers, achieving over 80% accuracy.
  • Clustering analysis was used to identify patterns and relationships within the age-disease data.

Main Results:

  • The Age-Phenome Knowledge-base (APK) was significantly augmented with new data on age-phenotype associations.
  • The text-mining pipeline demonstrated high accuracy in identifying and cataloging age-disease relationships.
  • Clustering analysis revealed both expected and novel associations between diseases based on age.

Conclusions:

  • The expanded APK provides a unique and valuable computational resource for researchers studying age-related diseases and phenotypes.
  • The developed text-mining approach offers an efficient and accurate method for curating biomedical knowledge on aging.
  • This work establishes a new framework for investigating complex age-phenotype relationships and advancing gerontological research.