Assigning species information to corresponding genes by a sequence labeling framework. (13th October 2022)

• Record Type: Journal Article
• Authors: Luo, Ling; Wei, Chih-Hsuan; Lai, Po-Ting; Chen, Qingyu; Islamaj, Rezarta; Lu, Zhiyong
• Is Part Of: Database. Volume 2022(2022)
• Publication Date: 2022-10-13
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baac090
• Languages: English
• ISSNs: 1758-0463

Abstract

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