A predictive model of gene expression using a deep learning framework

With an unprecedented amount of data available, it is important to explore new methods for developing predictive models to mine this data for scientific discoveries. In this study, we propose a deep learning regression model based on MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE) to predict gene expression from genotypes of genetic variation. Specifically, we use a stacked denoising auto-encoder to train our regression model in order to extract useful features, and utilize the multilayer perceptron for backpropagation. We further improve our model by adding a dropout technique to prevent overfitting. Our results on a real genomic dataset show that our MLP-SAE model with dropout outperform Lasso, Random Forests, and MLP-SAE without dropout. Our study provides a new application of deep learning in mining genomics data, and demonstrates that deep learning has great potentials in building predictive models to help understand biological systems.