PuMA: PubMed gene/cell type-relation Atlas
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces the PubMed Gene/Cell type-Relation Atlas (PuMA), a tool for literature-driven cell type annotation. PuMA extracts gene and cell type information from PubMed, aiding researchers in automated cell type identification.
Area Of Science
- Bioinformatics
- Computational Biology
- Genomics
Background
- Automated cell type annotation is crucial for single-cell analysis.
- Existing tools rely on curated databases or machine learning, but incorporating literature knowledge is underexplored.
Purpose Of The Study
- To develop a tool for literature-driven cell type annotation using PubMed.
- To facilitate the extraction and visualization of gene-cell type relationships from scientific literature.
Main Methods
- Developed the PubMed Gene/Cell type-Relation Atlas (PuMA) with a web interface.
- Utilized a machine learning-based named entity recognition model to extract gene and cell type concepts from PubMed.
- Linked biomedical ontologies and developed a ranking score for gene-cell type relations.
Main Results
- PuMA provides a searchable interface with interactive graph visualizations for exploring gene-cell type relations.
- Results are traceable to specific PubMed articles.
- PuMA demonstrated competitive performance against a manually curated database across mouse and human datasets.
Conclusions
- PuMA enables automated cell type annotation using PubMed articles, complementing existing databases.
- The software framework supports incremental knowledge updates through regular article imports.
- PuMA offers researchers a valuable tool for analyzing and automating cell type annotation based on the latest scientific literature.
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