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Semi-supervised learning improves gene expression-based prediction of cancer recurrence
Motivation: Gene expression profiling has shown great potential in outcome prediction for different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining robust and accurate classifiers. Traditional supervised learning techniques can only work with labeled data. Consequ...
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| Auteurs principaux: | , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Oxford University Press
2011
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3198572/ https://ncbi.nlm.nih.gov/pubmed/21893520 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/btr502 |
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