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Mining hidden knowledge: embedding models of cause–effect relationships curated from the biomedical literature
MOTIVATION: We explore the use of literature-curated signed causal gene expression and gene–function relationships to construct unsupervised embeddings of genes, biological functions and diseases. Our goal is to prioritize and predict activating and inhibiting functional associations of genes and to...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710590/ https://www.ncbi.nlm.nih.gov/pubmed/36699407 http://dx.doi.org/10.1093/bioadv/vbac022 |