A Stabilized Dense Network Approach for High-Dimensional Prediction

The limitations of traditional statistical methods on high-dimensional gene expression data for inferencing have motivated and expanded research in developing deep learning methods to handle such data more effectively. In this paper, we look into the multi-task learning problem on gene expression inferencing and propose a method that incorporates deep learning techniques and statistical methods and effectively enhances the predictive accuracy in training neural network models for high-dimensional data analysis. Unlike many multi-task learning methods that handle only a relatively small number of tasks, our proposed method demonstrates effective learning capabilities for large-scale tasks up to thousands. In addition, this novel approach is computationally efficient and reproducible, and is capable of producing competitive results with a much simpler network structure in comparison to adversarial methods. The advantages are evident based on the results of the experiments, which are conducted on datasets from the Gene Expression Omnibus and Genotype-Tissue Expression project, both of which provide worldwide range of data resources for gene expression studies and various other data applications.