ArticleBMC bioinformatics2010
LINNAEUS: a species name identification system for biomedical literature.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers, 1 of them a synthesis that pooled it.
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Who cites it
133 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.Bioinformatics advances · 2025Article
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- HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.Bioinformatics (Oxford, England) · 2024Article
- Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.Database : the journal of biological databases and curation · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- The changing landscape of text mining: a review of approaches for ecology and evolution.Proceedings. Biological sciences · 2024Review
- A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature.Interdisciplinary sciences, computational life sciences · 2024Article
- Explanatory argumentation in natural language for correct and incorrect medical diagnoses.Journal of biomedical semantics · 2024Article
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024Article
- Zero-shot evaluation of ChatGPT for food named-entity recognition and linking.Frontiers in nutrition · 2024Article
- Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species.Frontiers in artificial intelligence · 2024Article
73 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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Abstract
backgroundThe task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.
resultsIn this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers.
conclusionsLINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at http://linnaeus.sourceforge.net/.
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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.