Identifying brain disease genes via integrating brain imaging and molecular network

Identifying genes associate to brain diseases is crucial for uncovering the related biological mechanisms and for advancing therapeutic interventions. Due to the ongoing advancements in network-based computational approaches, biological networks, especially molecular networks, provide valuable insights for predicting disease genes. However, many methods have ignored brain imaging data when exploring brain disease genes, despite its widely use in neuroscience research. In this paper, we propose a novel framework, Deep Interactive AutoEncoder (DIAE), which integrates brain imaging and molecular-based gene networks to predict brain disease genes. DIAE first constructs a gene association network based on brain imaging and high-resolution whole brain-whole gene expression data. Subsequently, a deep and interactive multi-network integration method is introduced to learn low-dimensional features of genes by combining the brain imaging-based network with other molecular-based gene networks. Finally, these features are utilized to predict brain disease genes using a support vector machine (SVM) model. For performance evaluation, we compare DIAE with three existing state-of-the-art methods in the context of disease gene identification across four brain diseases. The experimental results show the superior performance of DIAE and highlight the effectiveness of the brain imaging-based gene network for predicting brain disease genes.