Hybrid curation of gene-mutation relations combining automated extraction and crowdsourcing.

ABSTRACT:

Background

This article describes capture of biological information using a hybrid approach that combines natural language processing to extract biological entities and crowdsourcing with annotators recruited via Amazon Mechanical Turk to judge correctness of candidate biological relations. These techniques were applied to extract gene- mutation relations from biomedical abstracts with the goal of supporting production scale capture of gene-mutation-disease findings as an open source resource for personalized medicine.

Results

The hybrid system could be configured to provide good performance for gene-mutation extraction (precision ∼82%; recall ∼70% against an expert-generated gold standard) at a cost of $0.76 per abstract. This demonstrates that crowd labor platforms such as

SUBMITTER: Burger JD 

PROVIDER: S-EPMC4170591 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature