ArticleBMC bioinformatics2019
BioReader: a text mining tool for performing classification of biomedical literature.
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 26 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
26 citing papers in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
- Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
- Article
- Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.Journal of Taibah University Medical Sciences · 2023Article
- Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives.Cardiovascular research · 2023Article
- Biologically informed deep learning to query gene programs in single-cell atlases.Nature cell biology · 2023Article
- An AI-enabled research support tool for the classification system of COVID-19.Frontiers in public health · 2023Article
- Pilot trial comparing COVID-19 publication database to conventional online search methods.BMJ health & care informatics · 2022Article
- 'Refbin' an online platform to extract and classify large-scale information: a pilot study of COVID-19 related papers.BMJ health & care informatics · 2022Article
- Computational Analyses Reveal Fundamental Properties of the Hemophilia Literature in the Last 6 Decades.Bioinformatics and biology insights · 2022Article
- Classifying domain-specific text documents containing ambiguous keywords.Database : the journal of biological databases and curation · 2021Article
- LitSuggest: a web-based system for literature recommendation and curation using machine learning.Nucleic acids research · 2021Article
- Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types.Journal of translational medicine · 2021Article
- Allogenic stem cell transplant-associated acute graft versus host disease: a computational drug discovery text mining approach using oral and gut microbiome signatures.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021Review
- Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms.Frontiers in immunology · 2021Review
- Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information.Frontiers in research metrics and analytics · 2021Article
- ThermoScan: Semi-automatic Identification of Protein Stability Data From PubMed.Frontiers in molecular biosciences · 2021Review
- Structure of communities in semantic networks of biomedical research on disparities in health and sexismBiomedica : revista del Instituto Nacional de Salud · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundScientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task.
resultsWe here present a tool that enables users to perform classification of scientific literature by text mining-based classification of article abstracts. BioReader (Biomedical Research Article Distiller) is trained by uploading article corpora for two training categories - e.g. one positive and one negative for content of interest - as well as one corpus of abstracts to be classified and/or a search string to query PubMed for articles. The corpora are submitted as lists of PubMed IDs and the abstracts are automatically downloaded from PubMed, preprocessed, and the unclassified corpus is classified using the best performing classification algorithm out of ten implemented algorithms.
conclusionBioReader supports data and information collection by implementing text mining-based classification of primary biomedical literature in a web interface, thus enabling curators and researchers to take advantage of the vast amounts of data and information in the published literature. BioReader outperforms existing tools with similar functionalities and expands the features used for mining literature in database curation efforts. The tool is freely available as a web service at http://www.cbs.dtu.dk/services/BioReader.
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.