CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relatio
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METHODOLOGY ARTICLE
Open Access
CGINet: graph convolutional network‑based model for identifying chemical‑gene interaction in an integrated multi‑relational graph Wei Wang1, Xi Yang1, Chengkun Wu1,2* and Canqun Yang1 *Correspondence: [email protected] 1 College of Computer, National University of Defense Technology, Changsha 410073, China Full list of author information is available at the end of the article
Abstract Background: Elucidation 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 chemicalgene interactions. Results: We 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. Conclusions: We 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. Keywords: Drug discovery, Chemical-gene interaction, Graph convolutional network, Integrated multi-relational graph
Background Drug discovery is a complex, lengthy, inefficient, and expensive process. The estimated average time needed to launch a new drug is around 10–15 years at an average cost of about $1.8 billion [1]. To expedite the drug development process, it is critical © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to t
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