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...

Descripción completa

Detalles Bibliográficos
Autores principales: Liang, Yong, Chai, Hua, Liu, Xiao-Ying, Xu, Zong-Ben, Zhang, Hai, Leung, Kwong-Sak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774162/
https://www.ncbi.nlm.nih.gov/pubmed/26932592
http://dx.doi.org/10.1186/s12920-016-0169-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774162/
https://www.ncbi.nlm.nih.gov/pubmed/26932592
http://dx.doi.org/10.1186/s12920-016-0169-6

Ejemplares similares