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Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L(1/2) regularization
BACKGROUND: One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients’ gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low...
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| Publicado no: | BMC Med Genomics |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4774162/ https://ncbi.nlm.nih.gov/pubmed/26932592 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12920-016-0169-6 |
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