Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees - Chou, Hsiu-Ling; Yao, Chung-Tay; Su, Sui-Lun; Lee, Chia-Yi; Hu, Kuang-Yu; Terng, Harn-Jing; Shih, Yun-Wen; Chang, Yu-Tien; Lu, Yu-Fen; Chang, Chi-Wen; Wahlqvist, Mark L.; Wetter, Thomas; et al. (BMC bioinformatics, 2013)
Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression.