Gene expression prediction using stacked temporal convolutional network

Predicting gene expression is one of the important tasks in molecular biology and genetics study. Studying the complex combinatorial code of gene expression could lead to a better understanding of gene regulation pattern i.e., how a gene increase or decrease specific gene products (protein and RNA) through translations. Such a pattern could be useful to study the origins of cancer, developing drugs for a certain disease, etc. In this study, we proposed to transform the Histone Modification data into one-dimensional space, and we predicted the gene expression by using Temporal Convolutional Networks. Previous studies proposed several methods ranging from classical machine learning approach (e.g., Support Vector Machine and Logistic Regression), as well as the most recent machine learning techniques (e.g., DeepChrome and DeepNN). Experiment results reveal that our approach is superior in terms of AUC score, accuracy, precision, recall, f-score, and specificity against the state-of-the-art-method, and only slightly worst in terms of precision and specificity against Support Vector Machine.