Cargando…
Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder
Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict...
Autores principales: | Gervits, Asia, Sharan, Roded |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755598/ https://www.ncbi.nlm.nih.gov/pubmed/36530386 http://dx.doi.org/10.3389/fbinf.2022.1025783 |
Ejemplares similares
-
DeepNC: a framework for drug-target interaction prediction with graph neural networks
por: Tran, Huu Ngoc Tran, et al.
Publicado: (2022) -
StrainFLAIR: strain-level profiling of metagenomic samples using variation graphs
por: Da Silva, Kévin, et al.
Publicado: (2021) -
Predicting circRNA-drug sensitivity associations via graph attention auto-encoder
por: Deng, Lei, et al.
Publicado: (2022) -
Finding melanoma drugs through a probabilistic knowledge graph
por: McCusker, Jamie Patricia, et al.
Publicado: (2017) -
De novo prediction of RNA–protein interactions with graph neural networks
por: Arora, Viplove, et al.
Publicado: (2022)