ArticleJournal of biomedical semantics2021
SIENA: Semi-automatic semantic enhancement of datasets using concept recognition.
Article in Journal of biomedical semantics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
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Who cites it
1 citing paper in PubMed.
- Aggregation of gene regulatory information and knowledge on FAIR principles enables discovery of pathogenic gene regulatory variants.Bioinformatics (Oxford, England) · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question.
resultsThis paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results.
conclusionsUsing concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions.
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Registered trials
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