A Machine-Learning Tool Concurrently Models Single Omics and Phenome Data for Functional Subtyping and Personalized Cancer Medicine

SIMPLE SUMMARY: Tumours are heterogeneous that reflect variable patient prognosis and treatment responses (phenotypes). Since these variable phenotypes are outcomes of genomics, it is essential to integrate genome and phenome jointly. In this study, we report the development and application of a new...

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Detalles Bibliográficos
Autores principales: Nyamundanda, Gift, Eason, Katherine, Guinney, Justin, Lord, Christopher J., Sadanandam, Anguraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601761/
https://www.ncbi.nlm.nih.gov/pubmed/33007815
http://dx.doi.org/10.3390/cancers12102811

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601761/
https://www.ncbi.nlm.nih.gov/pubmed/33007815
http://dx.doi.org/10.3390/cancers12102811

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