ArticleBioinformatics (Oxford, England)2016
Beyond accuracy: creating interoperable and scalable text-mining web services.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- dbGVOSCC: a comprehensive database of genetic variation for systems genetics research on oral squamous cell carcinoma.Frontiers in oncology · 2025Article
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
- GPDminer: a tool for extracting named entities and analyzing relations in biological literature.BMC bioinformatics · 2024Article
- Building a large gene expression-cancer knowledge base with limited human annotations.Database : the journal of biological databases and curation · 2023Article
- PubExN: An Automated PubMed Bulk Article Extractor with Affiliation Normalization Package.SN computer science · 2023Article
- tmVar 3.0: an improved variant concept recognition and normalization tool.Bioinformatics (Oxford, England) · 2022Article
- Integrated Approaches to Identify miRNA Biomarkers Associated with Cognitive Dysfunction in Multiple Sclerosis Using Text Mining, Gene Expression, Pathways, and GWAS.Diagnostics (Basel, Switzerland) · 2022Article
- GeneCup: mining PubMed and GWAS catalog for gene-keyword relationships.G3 (Bethesda, Md.) · 2022Article
- DES-Tcell is a knowledgebase for exploring immunology-related literature.Scientific reports · 2021Article
- Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.BMC genomics · 2020Article
- Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.Database : the journal of biological databases and curation · 2019Article
- tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine.Bioinformatics (Oxford, England) · 2018Article
- HiPub: translating PubMed and PMC texts to networks for knowledge discovery.Bioinformatics (Oxford, England) · 2016Article
- Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.Database : the journal of biological databases and curation · 2016Article
- PubMedPortable: A Framework for Supporting the Development of Text Mining Applications.PloS one · 2016Article
- Overview of the interactive task in BioCreative V.Database : the journal of biological databases and curation · 2016Article
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
unlabelledThe biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. AVAILABILITY AND IMPLEMENTATION: Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl CONTACT: : Zhiyong.Lu@nih.gov.
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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.