title: Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.
abstract: 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 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 survival time prediction is limited when such biological differences of the diseases have not been previously identified.
  
  
  METHODS
  To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease.
  
  
  RESULTS
  The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets.
  
  
  CONCLUSIONS
  The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.
authors.affiliation: State Key Laboratory of Quality Research in Chinese Medicines & Faculty of Information Technology, Macau University of Science and Technology, Macau, China. yliang@must.edu.mo.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liang
authors.affiliation: State Key Laboratory of Quality Research in Chinese Medicines & Faculty of Information Technology, Macau University of Science and Technology, Macau, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Chai
authors.affiliation: State Key Laboratory of Quality Research in Chinese Medicines & Faculty of Information Technology, Macau University of Science and Technology, Macau, China.
authors.forename: Xiao-Ying
authors.initials: XY
authors.lastname: Liu
authors.affiliation: Faculty of Science, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Zong-Ben
authors.initials: ZB
authors.lastname: Xu
authors.affiliation: Faculty of Science, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Hai
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of HongKong, HongKong, China.
authors.forename: Kwong-Sak
authors.initials: KS
authors.lastname: Leung
mesh_terms: D003198:Computer Simulation; D019992:Databases as Topic; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics / Q000401:mortality*; D020411:Oligonucleotide Array Sequence Analysis; D016016:Proportional Hazards Models; D016019:Survival Analysis