公开(公告)号：US20170024529A1

公开(公告)日：2017-01-26

申请号：US15219484

申请日：2016-07-26

Applicant: Macau University of Science and Technology

Inventor： Yong LIANG ,  Hua CHAI ,  Xiao-Ying LIU

IPC: G06F19/00 ,  G06N99/00 ,  G06F17/18 ,  G06N7/00

CPC classification number: G16H50/20 ,  G06N20/00 ,  G16B10/00 ,  G16B20/00 ,  G16B25/00 ,  G16B40/00 ,  Y02A90/26

Abstract: 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.

Abstract translation: 本发明提供了一种基于Cox模型和加速故障时间（AFT）模型的组合的新型半监督学习方法，其中每个模型对于高维和低样本量的生物数据进行L1 / 2正则化。 在这个半监督学习框架下，Cox模型可以尽可能多地分类“低风险”或“高风险”亚组，以提高其预测精度。 同时，AFT模型可以估计亚组中的检测数据，其中样本具有相同的分子基因型。 结合L1 / 2正则化，可以通过Cox模型和AFT模型选择一些基因，并且它们与癌症显着相关。