Identifying drug–target interactions based on graph convolutional network and deep neural network

Zhao, Tianyi; Hu, Yang; Valsdottir, Linda R.; Zang, Tianyi; Peng, Jiajie

doi:10.1093/bib/bbaa044

Cited by 197 publications

(108 citation statements)

References 46 publications

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“…Deep learning methods are widely used in the field of bioinformatics [13][14][15][16][17] nowadays. Since we could build a disease network, we used Graph Convolutional Network (GCN) [18] to extracted features from network.…”

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confidence: 99%

Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost

Wang

Yuan

et al. 2020

BMC Bioinformatics

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Background With the rapid development of medical treatment, many patients not only consider the survival time, but also care about the quality of life. Changes in physical, psychological and social functions after and during treatment have caused a lot of troubles to patients and their families. Based on the bio-psycho-social medical model theory, mental health plays an important role in treatment. Therefore, it is necessary for medical staff to know the diseases which have high potential to cause psychological trauma and social avoidance (PTSA). Results Firstly, we obtained diseases which can cause PTSA from literatures. Then, we calculated the similarities of related-diseases to build a disease network. The similarities between diseases were based on their known related genes. Then, we obtained these diseases-related proteins from UniProt. These proteins were extracted as the features of diseases. Therefore, in the disease network, each node denotes a disease and contains the information of its related proteins, and the edges of the network are the similarities of diseases. Then, graph convolutional network (GCN) was used to encode the disease network. In this way, each disease’s own feature and its relationship with other diseases were extracted. Finally, Xgboost was used to identify PTSA diseases. Conclusion We developed a novel method ‘GCN-Xgboost’ and compared it with some traditional methods. Using leave-one-out cross-validation, the AUC and AUPR were higher than some existing methods. In addition, case studies have been done to verify our results. We also discussed the trajectory of social avoidance and distress during acute survival of breast cancer patients.

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confidence: 99%

Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost

Wang

Yuan

et al. 2020

BMC Bioinformatics

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“…In recent years, researchers mainly use computational methods to realize drug repositioning because biological experimental methods cost a lot of money and time [8]. Now the most widely used computing methods include the following four categories: network-based methods, knowledge graph embedding-based method, text mining methods and biological feature-based methods [9].…”

Section: Related Workmentioning

confidence: 99%

“…These methods improve the accuracy of DTI prediction to a certain extent. However, these methods do not take drug-drug or protein-protein interactions into account [9].…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

et al. 2021

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The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.

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“…Such topological cell relation can be well captured by the Graph Convolutional Networks (GCN) [33]. Recently, GCN and its related methods have been successfully applied in single cell and disease [34][35][36][37], showing that inclusion of GCN enables to learn the high-order representation and topological relations of cells that improve performance.…”

Section: Introductionmentioning

confidence: 99%

scGCN: a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics

Song

Zhang

2020

Preprint

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Single-cell omics represent the fastest-growing genomics data type in the literature and the public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of the single-cell omics. The current label transfer methods have limited performance, largely due to the intrinsic heterogeneity and extrinsic differences between datasets. Here, we present a robust graph-based artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Benchmarked with other label transfer methods on totally 30 single cell omics datasets, scGCN has consistently demonstrated superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN.

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Identifying drug–target interactions based on graph convolutional network and deep neural network

Cited by 197 publications

References 46 publications

Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost

Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost

Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

scGCN: a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics

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