Integrating Feature Selection and Feature Extraction Methods With Deep Learning to Predict Clinical Outcome of Breast Cancer

In many microarray studies, classifiers have been constructed based on gene signatures to predict clinical outcomes for various cancer sufferers. However, signatures originating from different studies often suffer from poor robustness when used in the classification of data sets independent from which they were generated from. In this paper, we present an unsupervised feature learning framework by integrating a principal component analysis algorithm and autoencoder neural network to identify different characteristics from gene expression profiles. As the foundation for the obtained features, an ensemble classifier based on the AdaBoost algorithm (PCA-AE-Ada) was constructed to predict clinical outcomes in breast cancer. During the experiments, we established an additional classifier with the same classifier learning strategy (PCA-Ada) in order to perform as a baseline to the proposed method, where the only difference is the training inputs. The area under the receiver operating characteristic curve index, Matthews correlation coefficient index, accuracy, and other evaluation parameters of the proposed method were tested on several independent breast cancer data sets and compared with representative gene signature-based algorithms including the baseline method. Experimental results demonstrate that the proposed method using deep learning techniques performs better than others.