ArticleJournal of biomedical informatics2021
NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition.
Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 33 citations in OpenAlex.
- The DSMZ Digital Diversity Annotation Hub: a pipeline for database expansion via text mining and human curation.Journal of integrative bioinformatics · 2026Article
- What Do Biomedical NER and Entity Linking Benchmarks Measure? A Corpus-Centric Diagnostic Framework.ArXiv · 2026Article
- Cell phenotypes in the biomedical literature: a systematic analysis and text mining corpus.bioRxiv : the preprint server for biology · 2026Article
- Improving biomedical entity linking with generative relevance feedback.Bioinformatics (Oxford, England) · 2026Article
- ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and Mesh co-occurrence networks from context-specific biomedical literature.PLoS computational biology · 2025Article
- Assessing Artificial Intelligence (AI) Implementation for Assisting Gene Linking (at the National Library of Medicine).JAMIA open · 2025Article
- JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps.Database : the journal of biological databases and curation · 2024Article
- HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.Bioinformatics (Oxford, England) · 2024Article
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Ensemble pretrained language models to extract biomedical knowledge from literature.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop.Database : the journal of biological databases and curation · 2024Article
- BELHD: improving biomedical entity linking with homonym disambiguation.Bioinformatics (Oxford, England) · 2024Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- Advancing entity recognition in biomedicine via instruction tuning of large language models.Bioinformatics (Oxford, England) · 2024Article
- A Comprehensive Evaluation of Biomedical Entity Linking Models.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2023Article
- BELB: a biomedical entity linking benchmark.Bioinformatics (Oxford, England) · 2023Review
- 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
- The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition.International journal of molecular sciences · 2022Article
- Assigning species information to corresponding genes by a sequence labeling framework.Database : the journal of biological databases and curation · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
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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 ftp://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene. We have also applied this tool to the entire PubMed/PMC with their results freely accessible through our web-based tool PubTator (www.ncbi.nlm.nih.gov/research/pubtator).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.