Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework

TL;DR

This paper introduces a deep learning sequence-labeling framework for assigning species information to genes in research articles, significantly improving accuracy over traditional heuristic methods.

Contribution

The paper presents a novel deep learning-based sequence-labeling approach for gene-species assignment, outperforming rule-based methods in accuracy.

Findings

Accuracy improved from 65.8% to 81.3%.

Sequence-labeling reduces the number of pairs evaluated.

Open-source code and data available.

Abstract

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.