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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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書誌詳細
出版年:Cancers (Basel)
主要な著者: Nyamundanda, Gift, Eason, Katherine, Guinney, Justin, Lord, Christopher J., Sadanandam, Anguraj
フォーマット: Artigo
言語:Inglês
出版事項: MDPI 2020
主題:
オンライン・アクセス: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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