BioN∅T: A searchable database of biomedical negated sentences
Article Quality & Performance Metrics
Readership in this journal
SteadyRanked #95 of 835 articles by views in BMC Bioinformatics
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 835 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
Abstract
Abstract
Background
Negated biomedical events are often ignored by text-mining applications; however, such events carry scientific significance. We report on the development of BioN∅T, a database of negated sentences that can be used to extract such negated events.
Description
Currently BioN∅T incorporates ≈32 million negated sentences, extracted from over 336 million biomedical sentences from three resources: ≈2 million full-text biomedical articles in Elsevier and the PubMed Central, as well as ≈20 million abstracts in PubMed. We evaluated BioN∅T on three important genetic disorders: autism, Alzheimer's disease and Parkinson's disease, and found that BioN∅T is able to capture negated events that may be ignored by experts.
Conclusions
The BioN∅T database can be a useful resource for biomedical researchers. BioN∅T is freely available at
| Reference Key |
shashank2011biontbmc
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Shashank, Agarwal;Hong, Yu;Issac, Kohane; |
| Journal | BMC Bioinformatics |
| Year | 2011 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.