Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L<sub>1/2</sub> regularization
作者: Yong LiangHua ChaiXiao-Ying LiuZong-Ben XuHai ZhangKwong-Sak Leung
作者单位: 1Macau University of Science and Technology
2Xi’an Jiaotong University
3The Chinese University of HongKong
刊名: BMC Medical Genomics, 2016, Vol.9 (1)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12920-016-0169-6
关键词: Cancer survival analysisSemi-supervised learningGene selectionRegularizationCox proportional hazards modelAccelerated failure time model
英文摘要: Abstract(#br) Background(#br)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 risk classification or survival time prediction for the patients’ clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the...
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影响因子:3.466 (2012)

  • regularization 正则化
  • sub 分段
  • learning 学识
  • AFT Abnormal False Test
  • survival 生存
  • analysis 分析
  • based 基于
  • method 方法
  • Cancer 巨蟹座