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...

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Detalles Bibliográficos
Autores principales: Krämer, Andreas, Green, Jeff, Billaud, Jean-Noël, Pasare, Nicoleta Andreea, Jones, Martin, Tugendreich, Stuart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Original Paper
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

Internet

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

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