Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation.

ABSTRACT: Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expec

SUBMITTER: Clark AM 

PROVIDER: S-EPMC4137659 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature