Genescene: biomedical text and data mining

  • Authors:

  • Gondy Leroy;Hsinchun Chen;Jesse D. Martinez;Shauna Eggers;Ryan R. Falsey;Kerri L. Kislin;Zan Huang;Jiexun Li;Jie Xu;Daniel M. McDonald;Gavin Ng

  • Affiliations:

  • The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona;The University of Arizona

  • Venue:
  • Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.