Temporal Expression Prediction by Integrating Genome Dynamics via Spatio-temporal GNNs

Temporal gene expression is being analyzed via high-throughput profiling of molecular data over time. The expression values of genes are impacted by their previous expression values as well as the expression of interacting genes over time. Hi-C provides us with a broad genome-wide perspective on the interacting dynamics of genes. In this paper, we propose neural network-based spatio-temporal graph approaches STEPmr and STEPmi to predict changes in mRNA and miRNA expression over time, respectively. Both approaches can integrate a diverse set of Hi-C datasets and features obtained from Hi-C when predicting temporal expression patterns. Our methods can predict mRNA and miRNA expression with 77% and 93% correlation and ith mean squared errors of 0.21 and 0.01, explaining 59.1% and 88% of the variance, respectively. Important characteristics of the genes with the highest performances in both datasets are that they are structural signaling genes or transcriptional regulators involved in fundamental processes such as homeostasis, development, and RNA processing. Additionally, they are not limited to a specific cell type, but rather show constant expression throughout different tissues. In contrast, the lowest-performed genes generally behave in context-dependent expression patterns, where they include condition-specific biological functions instead of vital biological activities. These findings suggest a model of gene regulation and its predictability that is impacted by interacting gene dynamics. Our code and datasets are publicly available at https://github.com/seferlab/temporalhic .