ArticleJournal of cheminformatics2015
tmChem: a high performance approach for chemical named entity recognition and normalization.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Trial
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
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- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- Tracking the Spread of Pollen on Social Media Using Pollen-Related Messages From Twitter: Retrospective Analysis.Journal of medical Internet research · 2024Article
- Chemical entity normalization for successful translational development of Alzheimer's disease and dementia therapeutics.Journal of biomedical semantics · 2024Article
- Application of machine reading comprehension techniques for named entity recognition in materials science.Journal of cheminformatics · 2024Article
- Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023.Heliyon · 2024Review
- GPDminer: a tool for extracting named entities and analyzing relations in biological literature.BMC bioinformatics · 2024Article
- Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024Article
- OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.Database : the journal of biological databases and curation · 2023Article
- AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.Bioinformatics (Oxford, England) · 2023Article
- Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.Database : the journal of biological databases and curation · 2023Article
- A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition.BMC bioinformatics · 2023Article
- BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework.BMC bioinformatics · 2022Article
- Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics.Database : the journal of biological databases and curation · 2022Article
- Exploiting document graphs for inter sentence relation extraction.Journal of biomedical semantics · 2022Article
- A simple neural vector space model for medical concept normalization using concept embeddings.Journal of biomedical informatics · 2022Article
- Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science.Patterns (New York, N.Y.) · 2022Article
- MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.Metabolites · 2022Article
42 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Chemical compounds and drugs are an important class of entities in biomedical research with great potential in a wide range of applications, including clinical medicine. Locating chemical named entities in the literature is a useful step in chemical text mining pipelines for identifying the chemical mentions, their properties, and their relationships as discussed in the literature. We introduce the tmChem system, a chemical named entity recognizer created by combining two independent machine learning models in an ensemble. We use the corpus released as part of the recent CHEMDNER task to develop and evaluate tmChem, achieving a micro-averaged f-measure of 0.8739 on the CEM subtask (mention-level evaluation) and 0.8745 f-measure on the CDI subtask (abstract-level evaluation). We also report a high-recall combination (0.9212 for CEM and 0.9224 for CDI). tmChem achieved the highest f-measure reported in the CHEMDNER task for the CEM subtask, and the high recall variant achieved the highest recall on both the CEM and CDI tasks. We report that tmChem is a state-of-the-art tool for chemical named entity recognition and that performance for chemical named entity recognition has now tied (or exceeded) the performance previously reported for genes and diseases. Future research should focus on tighter integration between the named entity recognition and normalization steps for improved performance. The source code and a trained model for both models of tmChem is available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmChem. The results of running tmChem (Model 2) on PubMed are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator.
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Registered trials
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.