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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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| 出版年: | Cancers (Basel) |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2020
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| 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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