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

One of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several disc...

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Bibliografiska uppgifter
Huvudupphovsmän: Gift Nyamundanda, Katherine Eason, Justin Guinney, Christopher J. Lord, Anguraj Sadanandam
Materialtyp: Artigo
Språk:Inglês
Publicerad: MDPI AG 2020-09-01
Serie:Cancers
Ämnen:
machine learning
breast cancer
continuous subtypes
functional subtypes
CDK inhibitor
etoposide
Länkar:https://www.mdpi.com/2072-6694/12/10/2811
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