ArticleBMC bioinformatics2019
Solr-Plant: efficient extraction of plant names from text.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease.JMIR formative research · 2024Article
- Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network.Scientific reports · 2024Article
- WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data.Applications in plant sciences · 2020Article
- Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach.Bioinformatics and biology insights · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundThe retrieval of plant-related information is a challenging task due to variations in species name mentions as well as spelling or typographical errors across data sources. Scalable solutions are needed for identifying plant name mentions from text and resolving them to accepted taxonomic names.
resultsAn Apache Solr-based fuzzy matching system enhanced with the Smith-Waterman alignment algorithm ("Solr-Plant") was developed for mapping and resolution to a plant name and synonym thesaurus. Evaluation of Solr-Plant suggests promising results in terms of both accuracy and processing efficiency on misspelled species names from two benchmark datasets: (1) SALVIAS and (2) National Center for Biotechnology Information (NCBI) Taxonomy. Additional evaluation using S800 text corpus also reflects high precision and recall. The latest version of the source code is available at https://github.com/bcbi/SolrPlantAPI . A REST-compliant web interface and service for Solr-Plant is hosted at http://bcbi.brown.edu/solrplant .
conclusionAutomated techniques are needed for efficient and accurate identification of knowledge linked with biological scientific names. Solr-Plant complements the current state-of-the-art in terms of both efficiency and accuracy in identification of names restricted at species level. The approach can be extended to identify broader groups of organisms at different taxonomic levels. The results reflect potential utility of Solr-Plant as a data mining tool for extracting and correcting plant species names.
Indexed as
Identifiers
What OpenQuestion holds
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.