  The automatic assignment of species information to the corresponding genes in
a research article is a critically important step in the gene normalization
task, whereby a gene mention is normalized and linked to a database record or
identifier by a text-mining algorithm. Existing methods typically rely on
heuristic rules based on gene and species co-occurrence in the article, but
their accuracy is suboptimal. We therefore developed a high-performance method,
using a novel deep learning-based framework, to classify whether there is a
relation between a gene and a species. Instead of the traditional binary
classification framework in which all possible pairs of genes and species in
the same article are evaluated, we treat the problem as a sequence-labeling
task such that only a fraction of the pairs needs to be considered. Our
benchmarking results show that our approach obtains significantly higher
performance compared to that of the rule-based baseline method for the species
assignment task (from 65.8% to 81.3% in accuracy). The source code and data for
species assignment are freely available at
https://github.com/ncbi/SpeciesAssignment.
