A realistic assessment of methods for extracting gene/protein interactions from free text.

ABSTRACT:

Background

The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger.

Results

Our results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard) gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; an

SUBMITTER: Kabiljo R 

PROVIDER: S-EPMC2723093 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature