Fig. 6

The flowchart of the CGINet pipeline. a The framework of CGINet. The graph convolutional encoder takes the integrated multi-relational graph as input (the one-hot vectors for each node and the adjacency matrices) and returns a chemical embedding matrix and a gene embedding matrix. The tensor decomposition decoder uses these node embeddings to compute the probabilities of interactions between the chemicals and the candidate genes. b Graph convolutional encoder. We take the subgraph perspective as an example. Initial embeddings of chemicals \(c_{1}\) and genes \(g_{1}\) are learned with the binary association subgraph. For example, \(c_{1}\) receives information from neighbor nodes, including chemical nodes (\(c_{2}\), \(c_{3}\), \(c_{4}\)) and pathways (\(p_{1}\) \(p_{2}\)). The initial embeddings are then transferred to the multi-interaction subgraph for learning final embeddings. In the multi-interaction subgraph, the encoder aggregates information not only from the neighbor nodes across known edges but also from the new neighbors connected by latent links (shown in dotted line). For example, \(c_{1}\) encodes neighborhood information from \(g_{1}\), \(g_{2}\), \(g_{3}\) and \(g_{4}\)