ArticleDatabase : the journal of biological databases and curation2022
Assigning species information to corresponding genes by a sequence labeling framework.
Article in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Empowering biological knowledgebases: advances in human-in-the-loop AI-driven literature curation.Bioinformatics advances · 2026Review
- BELHD: improving biomedical entity linking with homonym disambiguation.Bioinformatics (Oxford, England) · 2024Article
- GNorm2: an improved gene name recognition and normalization system.Bioinformatics (Oxford, England) · 2023Article
- AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
6 authors.
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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 an 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 identify 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 65.8-81.3% in accuracy). The source code and data for species assignment are freely available. Database URL https://github.com/ncbi/SpeciesAssignment.
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