ReviewF1000Research2014
An analysis on the entity annotations in biological corpora.
Review in F1000Research, 2014. 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
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
7 citing papers in PubMed.
- 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
- NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature.Scientific data · 2021Article
- RCorp: a resource for chemical disease semantic extraction in Chinese.BMC medical informatics and decision making · 2019Article
- Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm.Journal of cheminformatics · 2019Article
- Gold-standard ontology-based anatomical annotation in the CRAFT Corpus.Database : the journal of biological databases and curation · 2017Article
- Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.BMC medical informatics and decision making · 2016Article
- BioCreative V CDR task corpus: a resource for chemical disease relation extraction.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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Collection of documents annotated with semantic entities and relationships are crucial resources to support development and evaluation of text mining solutions for the biomedical domain. Here I present an overview of 36 corpora and show an analysis on the semantic annotations they contain. Annotations for entity types were classified into six semantic groups and an overview on the semantic entities which can be found in each corpus is shown. Results show that while some semantic entities, such as genes, proteins and chemicals are consistently annotated in many collections, corpora available for diseases, variations and mutations are still few, in spite of their importance in the biological domain.
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.