Cancer drug response prediction with surrogate modeling-based graph neural architecture search

MOTIVATION: Understanding drug–response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building a...

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Detalles Bibliográficos
Autores principales: Oloulade, Babatounde Moctard, Gao, Jianliang, Chen, Jiamin, Al-Sabri, Raeed, Wu, Zhenpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432359/
https://www.ncbi.nlm.nih.gov/pubmed/37555809
http://dx.doi.org/10.1093/bioinformatics/btad478

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432359/
https://www.ncbi.nlm.nih.gov/pubmed/37555809
http://dx.doi.org/10.1093/bioinformatics/btad478

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