BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution

Xi Yang, Wei Wang, Jing-Lun Ma, Yan-Long Qiu, Kai Lu, Dong-Sheng Cao, Chengkun Wu

Abstract: Understanding chemical–gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions.