Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below

Extraction of gene-disease relations from Medline using domain dictionaries and machine learning.

Author information

  • 1Tsujii Laboratory, Room 615, 7th Building of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033, Japan. chun@is.s.u-tokyo.ac.jp

Abstract

We describe a system that extracts disease-gene relations from Medline. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction is heavily dependent upon the performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.

PMID:
17094223
[PubMed - indexed for MEDLINE]
Free full text
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Pacific Sympsium On Biocomputing
    Loading ...
    Write to the Help Desk