Fig. 2

Overview of the M-GBBD framework. The framework takes two brain heterogeneous graphs, namely \({{\text{A}}}_{{\text{GPR}}}\) and \({{\text{A}}}_{{\text{GTR}}}\) (top left) as input. To reduce dimensionality and extract gene primary features along with spatial distributions, a multi-layer DNN is employed. The Kullback–Leibler divergence is utilized to calculate and learn the distributions of common subspace. After iterative optimization, an eBFC-based gene network is obtained. By combining the eBFC-based gene network with the D-D network and G-D network, GCN is applied to learn representations of genes and diseases. Finally, these representations are fed into MLP for predicting gene-disease associations