Using uncertainty to link and rank evidence from biomedical literature for model curation
Abstract
In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 24, 2017
- Source ID
- 10.1093/bioinformatics/btx466
Entities
People
- Chrysoula Zerva
- Philip Day
- Riza Batista-navarro
- Sophia Ananiadou
Organizations
- Biotechnology and Biological Sciences Research Council
- Defense Advanced Research Projects Agency
- Engineering and Physical Sciences Research Council
- University of Manchester