COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data

MOTIVATION: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that are optimized for large datasets usually operate a...

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Detalles Bibliográficos
Autores principales: Ditz, Jonas C, Reuter, Bernhard, Pfeifer, Nico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Biomedical Informatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311322/
https://www.ncbi.nlm.nih.gov/pubmed/37387152
http://dx.doi.org/10.1093/bioinformatics/btad204

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311322/
https://www.ncbi.nlm.nih.gov/pubmed/37387152
http://dx.doi.org/10.1093/bioinformatics/btad204

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