Nebnet:Exploiting Node-Edge Bi-Level Network for Gene Expression Prediction

Spatial Transcriptomics (ST) has made great progress in breast cancer due to it captures gene expression with fine-grained spots. It has always been low-throughout owing to reliance on special and pricey technologies. Recently, numerous types of models focus on predicting gene expression in windows (i.e., spots) on tissue images, aiming to provide alternative ST data. However, in these models, the interrelation information between windows is not well considered. We propose a Node-Edge Bi-level Network (NebNet ) for gene expression prediction within tissue slide images. Our model learns inter-relation information among windows, by using our message passing mechanism. Using NebNet, without any additional setting, experiments conducted on 10x Genomics breast cancer data show that our NebNet achieves an impressive PCC@S of 8.26 for gene expression prediction. This performance exceeds the current state-of-the-art model by nearly 6.6%. Code is available at https://github.com/biyecc/NebNet.