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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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| Pubblicato in: | Cancers (Basel) |
|---|---|
| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7601761/ https://ncbi.nlm.nih.gov/pubmed/33007815 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/cancers12102811 |
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