An integrative analysis system of gene expression using self-paced learning and SCAD-Net - Liang Yong

Background Few proposed gene biomarkers have been satisfactory in clinical applications. That is mainly due to the small studies sample size. Because of the batch effect, different gene-expression studies cannot be merged directly. Many integrative methods have attempted to integrate various datasets to eliminate the batch effect while keeping biological information intact. However, due to the complexity of the batch effect, it cannot be eliminated, and these methods may even add new systematic errors to the data, further complicating integrated data. Therefore, direct analysis of the merged data may cause some issues. In this paper, we suggest a novel integrative analysis framework for merged gene-expression data. The framework adopts the self-paced learning. This method allows samples to be automatically added into the training period, from simple to intricate, in a purely self-paced way. Moreover, the …