Abstract
BackgroundElucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions.ResultsWe present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types.ConclusionsWe study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.
Highlights
Elucidation of interactive relation between chemicals and genes is of key relevance for discovering new drug leads in drug development and for repositioning existing drugs to novel therapeutic targets
One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned with the binary association subgraphs and transferred to the multi-interaction subgraph for final node embeddings learning
We individually measure the performance of each interaction type using area under the receiver-operating characteristic (AUROC), area under the precision-recall curve (AUPRC), and average precision for the top-k identifications (AP@k)
Summary
Elucidation of interactive relation between chemicals and genes is of key relevance for discovering new drug leads in drug development and for repositioning existing drugs to novel therapeutic targets. Elucidation of interactive relation between chemicals and genes, named chemical-gene interactions (CGIs), is of key relevance for discovering new drug leads in drug development and for repositioning existing drugs to novel therapeutic targets. The PubChem database contains more than 30 million chemicals, but few have confirmed gene targets [7] This predicament drives the imperative need for automatic and efficient methods to infer chemical-gene interactions as a preliminary process rather than experimentally determining every possible chemical-gene pair, which is time-consuming and costly. According to different kinds of data used, we roughly divide the computational methods for CGI prediction into three categories: biomedical literature-based, molecular structure-based, and biological network-based
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