Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
This work addresses a specific bottleneck in biomedical text mining for researchers and databases, offering a more accurate method for linking genes to species, though it is incremental relative to existing approaches.
The paper tackles the problem of automatically assigning species information to genes in research articles, a key step in gene normalization, by introducing a deep learning-based sequence labeling framework that improves accuracy from 65.8% to 81.3% compared to rule-based methods.
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.