ArticleNPJ systems biology and applications2021
NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- Dementia-related volumetric assessments in neuroradiology reports: a natural language processing-based study.BMJ open · 2025Article
- Contrastive learning and mixture of experts enables precise vector embeddings in biological databases.Scientific reports · 2025Article
- A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Lived experience at the core: A classification system for risk-taking behaviours in bipolar.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
Abstract
Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades
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.