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A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data
BACKGROUND: Modern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medicine tests for unmet clinical needs from this information in the small cohorts a...
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| Publicado en: | BMC Bioinformatics |
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| Autores principales: | , , , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
BioMed Central
2019
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6567499/ https://ncbi.nlm.nih.gov/pubmed/31196002 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-019-2922-2 |
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