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DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile

Title
DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile
Authors
HanJiyeonKangMin JiLeeSanghyuk
Ewha Authors
이상혁
SCOPUS Author ID
이상혁scopus
Issue Date
2024
Journal Title
Computers in Biology and Medicine
ISSN
0010-4825JCR Link
Citation
Computers in Biology and Medicine vol. 174
Keywords
Deep neural networkDrug combinationDrug synergyDrug-induced expression profileGraph convolutional network
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods. © 2024 The Authors
DOI
10.1016/j.compbiomed.2024.108436
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자연과학대학 > 생명과학전공 > Journal papers
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