ArticleDatabase : the journal of biological databases and curation2016
Mining chemical patents with an ensemble of open systems.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Retrieval augmented scientific claim verification.JAMIA open · 2024Article
- NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature.Scientific data · 2021Article
- Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study.JMIR medical informatics · 2019Article
- Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017.Journal of medical Internet research · 2019Article
- A neural network approach to chemical and gene/protein entity recognition in patents.Journal of cheminformatics · 2018Article
- Statistical principle-based approach for gene and protein related object recognition.Journal of cheminformatics · 2018Article
- Extracting chemical-protein relations with ensembles of SVM and deep learning models.Database : the journal of biological databases and curation · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The significant amount of medicinal chemistry information contained in patents makes them an attractive target for text mining. In this manuscript, we describe systems for named entity recognition (NER) of chemicals and genes/proteins in patents, using the CEMP (for chemicals) and GPRO (for genes/proteins) corpora provided by the CHEMDNER task at BioCreative V. Our chemical NER system is an ensemble of five open systems, including both versions of tmChem, our previous work on chemical NER. Their output is combined using a machine learning classification approach. Our chemical NER system obtained 0.8752 precision and 0.9129 recall, for 0.8937 f-score on the CEMP task. Our gene/protein NER system is an extension of our previous work for gene and protein NER, GNormPlus. This system obtained a performance of 0.8143 precision and 0.8141 recall, for 0.8137 f-score on the GPRO task. Both systems achieved the highest performance in their respective tasks at BioCreative V. We conclude that an ensemble of independently-created open systems is sufficiently diverse to significantly improve performance over any individual system, even when they use a similar approach.Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/.
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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.