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NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition

Rezarta Islamaj, Chih-Hsuan Wei, David Cissel, Nicholas Miliaras, Olga Printseva, Oleg Rodionov, Keiko Sekiya, Janice Ward,
Published: 1 June 2021
Journal of Biomedical Informatics , Volume 118; doi:10.1016/j.jbi.2021.103779

Abstract: The automatic recognition of gene names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. While current methods for tagging gene entities have been developed for biomedical literature, their performance on species other than human is substantially lower due to the lack of annotation data. We therefore present the NLM-Gene corpus, a high-quality manually annotated corpus for genes developed at the US National Library of Medicine (NLM), covering ambiguous gene names, with an average of 29 gene mentions (10 unique identifiers) per document, and a broader representation of different species (including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, etc.) when compared to previous gene annotation corpora. NLM-Gene consists of 550 PubMed abstracts from 156 biomedical journals, doubly annotated by six experienced NLM indexers, randomly paired for each document to control for bias. The annotators worked in three annotation rounds until they reached complete agreement. This gold-standard corpus can serve as a benchmark to develop & test new gene text mining algorithms. Using this new resource, we have developed a new gene finding algorithm based on deep learning which improved both on precision and recall from existing tools. The NLM-Gene annotated corpus is freely available at We have also applied this tool to the entire PubMed/PMC with their results freely accessible through our web-based tool PubTator (
Keywords: Manual annotation / Gene entity recognition / Natural language processing / Biomedical Text Mining / Deep Learning

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