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Featured Article: Genotation: Actionable knowledge for the scientific reader.

Panduka Nagahawatte1, Ethan Willis2, Mark Sakauye2

  • 1Center of Biomedical Informatics, Memphis, TN 38103, USA panduka@uthsc.edu.

Experimental Biology and Medicine (Maywood, N.J.)
|February 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces Genomine and GenoDB, an application that links genomics concepts in articles to biomedical databases. It aids researchers in knowledge discovery and hypothesis generation for human health.

Keywords:
Genomic supplementsannotation geneannotation proteinjournal article viewerpharmacogenomics

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Bioinformatics

Background:

  • High-throughput data streams accelerate scientific discovery.
  • Diverse biomedical databases pose challenges for researchers.
  • An integrated informatics solution is needed for efficient knowledge discovery.

Purpose of the Study:

  • To present an article viewer application for discovering and sharing genomics-related knowledge.
  • To link concepts in scientific articles to disparate biomedical databases.
  • To provide researchers with actionable insights for hypothesis generation.

Main Methods:

  • Utilized the Genomine text mining algorithm to identify gene symbols from journal articles.
  • Integrated identified gene symbols with information from the self-updating GenoDB knowledgebase.
  • Developed a web application accessible via a browser for enhanced article viewing.

Main Results:

  • The Genomine algorithm achieved a 0.65 F-Score for gene symbol identification.
  • GenoDB contains data on 59,905 gene symbols, 5633 drug-gene relationships, 5981 gene-disease relationships, and 713 pathways.
  • The application enables visualization and sharing of supplementary information.

Conclusions:

  • The application provides convenient access to complex biological information.
  • Facilitates the generation of novel hypotheses to advance human health research.
  • Integrates genomic, proteomic, and pharmacogenomic data for automated knowledge discovery.