Comparison of gCDA's performance with the performance of three other classification methods.

Comparison of the performance of gCDA with the performance obtained with RDA, SVM and NB-SVM. For NB-SVM and gCDA, we chose to integrate the GRNs inferred with ARACNE. In this table are presented the mean (standard deviation) of the good classification rate over 100 MCCV iterations.

本研究对比了gCDA与RDA、支持向量机（SVM）、朴素贝叶斯支持向量机（NB-SVM）的模型性能。针对NB-SVM与gCDA，我们选用了通过ARACNE推断得到的基因调控网络（Gene Regulatory Networks, GRNs）进行整合。本表格呈现了100次蒙特卡洛交叉验证（Monte Carlo Cross Validation, MCCV）迭代下，分类正确率的均值（标准差）。