Using uncertainty to link and rank evidence from biomedical literature for model curation.

ABSTRACT:

Motivation

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 p

SUBMITTER: Zerva C 

PROVIDER: S-EPMC5860317 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature