Improving the generalization of unsupervised feature learning by using data from different sources on gene expression data for cancer diagnosis.

Machine learning techniques have been utilized on gene expression profiling for cancer diagnosis. However, the gene expression data suffer from the curse of high dimensionality. Different kinds of feature reduction methods have been proposed to decrease the features for specific cancer diagnosis. However, with the difficulty of obtaining the samples of a particular tumor, the lack of training samples may lead to the overfitting problem. In addition, the feature reduction model on a specific tumor may lead to the problem that the model is not scalable and cannot be generalized to new cancer types. To handle these problems, this paper proposes an unsupervised feature learning method to reduce the data dimensionality of gene expression data. This method amplifies the training samples of feature learning by utilizing the unlabeled samples from different sources. Two heuristic rules are devised to check if the unlabeled samples could be used for amplifying the training set. The amplified training set is used to train the feature learning model based on sparse autoencoder. Since the method leverages the knowledge among the expression data from different sources, it improves the generalization of unsupervised feature learning and further boosts the cancer diagnosis performance. A series of experiments are carried out on the gene expression datasets from TCGA and other sources. Experimental results prove that our method improves the generalization of cancer diagnosis when unlabeled data are used for latent feature learning. The flowchart of our proposed feature learning method.