Gene Expression Prediction Using a Deep 1D Convolution Neural Network

Histones are proteins around which DNA is coiled to form chromatin fibres in the nucleus of a biological cell. They undergo modifications post-translation, a process which produces proteins using the DNA as the blueprint. These modifications play a very important role in regulating gene expression by influencing the translation process. The knowledge of how such modifications affect gene expression and the need for an accurate pipeline to predict the expression values from modification signals is undeniable. In this paper, we present the first generalized deep learning model to classify gene expression based on the histone modification signals irrespective of the type of cell from which the signal was recorded. Our approach automatically performs feature extraction using ID convolutional layers which are used further to establish relationships among the learned features and make accurate predictions. This model is able to make predictions on all the different cell types by being trained only once. It also outperforms the present state of the art when compared against the predictions made for different kinds of cells and the computational resources required.