Deep graph embedding for prioritizing synergistic anticancer drug combinations

Jiang, Peiran; Huang, Shujun; Fu, Zhenyuan; Sun, Zexuan; Lakowski, Ted M.; Hu, Pingzhao

doi:10.1016/j.csbj.2020.02.006

Cited by 79 publications

(56 citation statements)

References 58 publications

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“…GEDFN approach recently has been used to solve various biomedical problems such as identifying microbial biomarkers from operational taxonomic unit abundance [22], predicting clinical outcomes [23] and prioritizing synergistic anticancer drug combinations. [24].…”

Section: Related Work On Graph Embeddingmentioning

confidence: 99%

Improved Prediction of Cancer Outcome Using Graph-Embedded Generative Adversarial Networks

Park

Choi

et al. 2021

IEEE Access

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Precise prognosis of cancer patients is important because it is associated with suggesting appropriate therapeutic strategies. Several computational and statistical methods have been proposed, but further improvement of these methods in terms of prediction accuracy is required. This paper presents a deep learning-based method for learning networks of prognostic genes, instead of only individual biomarker genes, for more accurate cancer prognosis prediction. This method utilizes generative adversarial networks, where the generator uses a biological network instead of a traditional fully connected network to learn the distributions of gene expression (mRNA), copy number variation, single nucleotide polymorphism, and DNA methylation data from cancer patients. The proposed model was applied to seven cancer types and exhibited higher prediction accuracy as compared to the existing state-of-the-art methods. On average, the area under the curve (AUC) was improved by 4% compared to the best performing existing methods for seven cancer types. In particular, for pancreatic adenocarcinoma, AUC was improved by 27.9%. The identified prognostic genes were reproducible and functionally meaningful. To the best of our knowledge, the proposed method represents the first attempt to learn genetic networks from multi-omics data. INDEX TERMS Graph-Embedded Generative Adversarial Networks, Prediction of cancer prognosis, Multi-omics integrated prediction model, Discovery of prognostic genes

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Section: Related Work On Graph Embeddingmentioning

confidence: 99%

Improved Prediction of Cancer Outcome Using Graph-Embedded Generative Adversarial Networks

Park

Choi

et al. 2021

IEEE Access

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“…There are also DDI studies specific to a group of drugs or to drugs used to treat a specific disease, rather than focusing on the entire drug network. For example, various studies have been conducted on cancer prevention drugs 28 , 29 ; others tackled high blood pressure patients; and some medications used in the treatment 30 , 31 . In these studies, only the risks and interactions for the examined group are calculated.…”

Section: Related Workmentioning

confidence: 99%

Identifying side effects of commonly used drugs in the treatment of Covid 19

Aygün

Kaya

Alhajj

2020

Sci Rep

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To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. As a result of the experiments, it has been found that the hematopoietic system and the cardiovascular system are exposed to more side effects than other organs. Among the focused drugs, Heparin and Atazanavir appear to cause more adverse reactions than other drugs. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

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“…There are also DDI studies speci c to a group of drugs or to drugs used to treat a speci c disease, rather than focusing on the entire drug network. For example; various studies have been conducted on cancer prevention drugs, e.g., (Jiang et al, 2020;Preuer et al, 2018) or on high blood pressure patients or medications used in the treatment, e.g., (Bacic-Vrca et al, 2010;Martha et al, 2015). In these studies, only the risks and interactions for the examined group are calculated.…”

Section: B Computer Based Ddi Studiesmentioning

confidence: 99%

Identifying Side Effects of Some Drugs Used in Covid-19 Treatment

Aygün

Kaya

Alhajj

2020

Preprint

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To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. Organ systems and diseases in which these 8 drugs cause the most negative effects have been identified. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

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Deep graph embedding for prioritizing synergistic anticancer drug combinations

Cited by 79 publications

References 58 publications

Improved Prediction of Cancer Outcome Using Graph-Embedded Generative Adversarial Networks

Improved Prediction of Cancer Outcome Using Graph-Embedded Generative Adversarial Networks

Identifying side effects of commonly used drugs in the treatment of Covid 19

Identifying Side Effects of Some Drugs Used in Covid-19 Treatment

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