A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis

Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such infor...

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Detalles Bibliográficos
Autores principales: Chai, Hua, Li, Zi-na, Meng, De-yu, Xia, Liang-yong, Liang, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638936/
https://www.ncbi.nlm.nih.gov/pubmed/29026100
http://dx.doi.org/10.1038/s41598-017-13133-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638936/
https://www.ncbi.nlm.nih.gov/pubmed/29026100
http://dx.doi.org/10.1038/s41598-017-13133-5

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