Cancer survival analysis using semisupervised learning method based on Cox and AFT models with L1/2 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 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.
I am doing my Master in computational Science. I am doing research to complete my thesis research. In my research I need to implement above-mentioned methods on Colorectal Cancer datasets