Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework

• URL: http://arxiv.org/abs/2205.03853v1
• Date: Sun, 8 May 2022 12:39:45 GMT
• Title: Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
• Authors: Ling Luo, Chih-Hsuan Wei, Po-Ting Lai, Qingyu Chen, Rezarta Islamaj Do\u{g}an, Zhiyong Lu
• Abstract summary: Existing methods typically rely on rules based on gene and species co-occurrence in the article. We develop a high-performance method, using a novel deep learning-based framework, to classify whether there is a relation between a gene and a species. Our benchmarking results show that our approach obtains significantly higher performance compared to that of the rule-based baseline method.
• Score: 7.231921004060877
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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