Deep graph embedding for prioritizing synergistic anticancer drug combinations

Computational and Structural Biotechnology Journal
Peiran JiangPingzhao Hu

Abstract

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0....Continue Reading

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Citations

Nov 11, 2020·Briefings in Bioinformatics·Giulia MuzioKarsten Borgwardt
Dec 11, 2020·Scientific Reports·İrfan AygünReda Alhajj
Aug 12, 2021·Computational and Structural Biotechnology Journal·Raihan RafiqueJulhash U Kazi
Sep 4, 2021·Briefings in Bioinformatics·Lianlian WuXiaochen Bo
Sep 29, 2021·Genome Medicine·Khoa A TranNicola Waddell

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Methods Mentioned

BETA
xenograft

Software Mentioned

Decagon
learn
XGBoost
DrugComb
DeepSynergy
SNE
AuDNNsynergy
PaDEL
scikit
GCN

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