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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Bibliografiske detaljer
Udgivet i:BMC Med Genomics
Main Authors: Liang, Yong, Chai, Hua, Liu, Xiao-Ying, Xu, Zong-Ben, Zhang, Hai, Leung, Kwong-Sak
Format: Artigo
Sprog:Inglês
Udgivet: BioMed Central 2016
Fag:
Research Article
Online adgang: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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