Yan Fang-Rong, Lin Jin-Guan, Liu Yu
Department of Mathematics, Southeast University, Nanjing 210096, China.
J Biomed Biotechnol. 2011;2011:875309. doi: 10.1155/2011/875309. Epub 2011 Jun 1.
The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis.
本研究的目的是使用数学模型找出肝纤维化进展与某些血清标志物水平之间的定量关系。我们通过使用平滑截断绝对偏差（SCAD）惩罚函数的稀疏逻辑回归来诊断大鼠肝纤维化。它不仅能给出高精度的稀疏解，还能为用户提供带有类别信息的精确分类概率。在模拟案例和实验案例中，通过使用贝叶斯自助法估计曲线下面积（AUC）的诊断敏感性的受试者工作特征（ROC）曲线，将所提出的方法与逐步线性判别分析（SLDA）以及带有最小绝对收缩和选择算子（LASSO）惩罚的稀疏逻辑回归进行比较，以选择变量。结果表明，新方法在大鼠血清标志物水平与硫代乙酰胺（TAA）诱导的肝纤维化之间提供了良好的相关性。同时，该方法也可能用于预测肝硬化的发展。
