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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2023
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| Materias: | |
| 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 |