Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment

MOTIVATION: Despite the success of recent machine learning algorithms’ applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying...

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Detalles Bibliográficos
Autores principales: Cho, Hyun Jae, Shu, Mia, Bekiranov, Stefan, Zang, Chongzhi, Zhang, Aidong
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/PMC10079355/
https://www.ncbi.nlm.nih.gov/pubmed/36864611
http://dx.doi.org/10.1093/bioinformatics/btad113

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079355/
https://www.ncbi.nlm.nih.gov/pubmed/36864611
http://dx.doi.org/10.1093/bioinformatics/btad113

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