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Semi-Supervised Learning Framework based on Cox and AFT Models with L1/2 Regularization for Patient's Survival Prediction
Semi-Supervised Learning Framework based on Cox and AFT Models with L1/2 Regularization for Patient's Survival Prediction
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机译：基于Cox和AFT模型的L1 / 2正则化半监督学习框架，用于患者生存预测
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摘要
The present invention provides a novel semi-supervised learning method based on the combination of the Cox model and the accelerated failure time (AFT) model, each of which is regularized with L1/2 regularization for high-dimensional and low sample size biological data. In this semi-supervised learning framework, the Cox model can classify the “low-risk” or a “high-risk” subgroup though samples as many as possible to improve its predictive accuracy. Meanwhile, the AFT model can estimate the censored data in the subgroup, in which the samples have the same molecular genotype. Combined with L1/2 regularization, some genes can be selected by the Cox model and the AFT model and they are significantly relevant with the cancer.
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