基于自主学习与SCAD-Net正则化的回归模型

众多基因生物标志物选择方法常因研究样本较少而不能直接用于临床诊断.于是有学者提出整合不同基因表达数据同时保留生物信息完整性的方法.然而,由于存在批量效应,导致直接整合不同基因表达数据可能会增加新的系统误差.针对上述问题,提出一个融合自主学习与SCAD-Net正则化的分析框架.一方面,自主学习方法能够先从低噪声样本中学习出基础模型,然后再通过高噪声样本学习使得模型更加稳健,从而避免批量效应;另一方面,SCAD-Net正则化融合了基因表达数据与基因间的交互信息,可以实现更好的特征选择效果.不同情形下的模拟数据以及在乳腺癌细胞系数据集上的结果表明,基于自主学习与SCAD-Net正则化的回归模型在处理高维复杂网络数据集时具有更好的预测效果.

Regression Model with Self-Paced Learning and SCAD-Net Regularization

Many methods for gene biomarker selection can not be directly used in clinical diagnosis because of a small number of research samples. Therefore, some scholars proposed methods of integrating different gene expression data while preserving the integrity of biological information. However, due to the batch effect, direct integration of different gene expression data may bring new systematic errors. In response to the above problems, an analysis framework integrating self-paced learning and SCAD-Net regularization is proposed. On the one hand, self-paced learning can learn the basic model from low-noise samples and then make the model more robust through high-noise samples to avoid batch effect. On the other hand, SCAD-Net regularization combines biological interaction information and gene expression data, which can achieve a better performance in feature selection. The simulation data in different cases and the results on the breast cancer cell line dataset show that the regression model based on self-paced learning and SCAD-Net regularization obtains better prediction results when dealing with high-dimensional complex network datasets.