Dynamic Graph Neural Network Learning for Temporal Omics Data Prediction

Abstract

High-throughput studies of biological systems are rapidly generating a wealth of 'omics'-scale data. Many of these studies are temporal collecting proteomics and genomics data capturing dynamic observations. While temporal omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values due to technical and experimental reasons. Data prediction methods, i.e., imputation and forecasting, have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. In this paper, we propose a graph-based method for temporal omics data imputation and forecasting that handle omics data containing missing values at multiple time points. The method takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform temporal imputation and forecasting using a long short-term memory network. Evaluating the proposed method on two real-world datasets demonstrated a distinct advantage over existing data imputation and forecasting methods. On the omics dataset, the average mean square error of our method improved 11.3% for imputation and 6.4% for forecasting compared to the baseline methods.