A graph based deep learning technology application in degenerative polyarthritis associated genes prediction

Abstract

Abstract: Degenerative polyarthritis is the most common joint disease and affects millions of people worldwide. However, there is currently no cure for degenerative polyarthritis and no effective methods to prevent or slow down its progression. Gene regulatory relationships are vital for understanding disease mechanisms and developing treatment and novel drugs. Gene regulatory networks can be obtained from the RNA sequencing. Although various single-cell and bulk RNA sequencing data are available, an effective method to integrate the data for molecular diagnosis and treatment of degenerative polyarthritis has not yet been carried out. Here, we propose a novel deep learning-based method to efficiently capture the gene regulatory features of degenerative polyarthritis. First, we integrate single-cell RNA sequencing data-based gene regulatory network to model the gene regulatory relationships between genes and transcription factors as node feature aggregation. Second, we propose a graph convolutional model named dpTF-GCN on gene regulatory graph to transmit and update the node feature for potential associated genes predicting. According to the results, dpTF-GCN achieved the best performance among represented network-based methods. Furthermore, case studies suggest that dpTF-GCN can identify potential associated genes accurately. Our research not only provides theoretical and methodological support for the study of degenerative polyarthritis, but also provides a research case for the application of graph neural network-based identification of associated genes in other diseases.