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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...
Gorde:
| Egile Nagusiak: | , , , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
MDPI AG
2020-09-01
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| Saila: | Cancers |
| Gaiak: | |
| Sarrera elektronikoa: | https://www.mdpi.com/2072-6694/12/10/2811 |
| Etiketak: |
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