GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

Sang, Shengtian; Yang, Zhihao; Liu, Xiaoxia; Wang, Lei; Lin, Hongfei; Wang, Jian; Dumontier, Michel

doi:10.1109/access.2018.2886311

Cited by 56 publications

(46 citation statements)

References 34 publications

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“…Then, the loss error is calculated by the score function, and training is performed. GrEDeL [19] uses TransE to learn the embedding vector based on a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the Long Short-Term Memory (LSTM) Networks model is trained to discover candidate drugs for diseases of interest from biomedical literature.…”

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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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 latter publication presents SemaTyP, a method for discovering drug-disease relations based on a literature Knowledge Graph. Its successor, GrEDeL [16] extends the previous model by employing graph embedding techniques and deep learning approaches.…”

Section: Related Workmentioning

confidence: 99%

Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph

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et al. 2020

Artificial Intelligence in Medicine

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Knowledge Graphs provide insights from data extracted in various domains. In this paper, we present a new approach to discover probable drug-to-drug interactions, through the generation of a Knowledge Graph from disease-specific literature. The Graph is generated using natural language processing and semantic indexing of open biomedical publications and manually annotated resources. Then, the semantic paths connecting different drugs in the Graph are extracted and aggregated into feature vectors representing drug pairs. Finally, a classifier is trained on known interactions and is used to discover other possible interacting drug pairs. We evaluate this approach on two use cases, Alzheimer's Disease and Lung Cancer. A manually curated drug database is utilized as a golden standard and our system is shown to outperform competing graph embedding approaches, while also recommending new drug-drug interactions that are validated retrospectively.

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“…For example, Sang et al [20] used a deep learning architecture to convert graph nodes into embeddings for extracting semantic relation between nodes. More precisely, node embedding algorithms [21]- [25], which aim to automatically extract features from graphstructured data and learn the representations for nodes in the graph, have been successfully applied to multi-label classification, link prediction and visualization tasks. In particular, the Node2vec [24] can capture the diversity of connectivity patterns observed in large scale networks and automatically learns a mapping of nodes to a low-dimensional space of features.…”

Section: ) Node Embedding Similaritiesmentioning

confidence: 99%

Using Alias Sampling Strategy Based on Network Embeddings to Detect Protein Complexes

2020

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Detecting protein complexes from available protein-protein interaction (PPI) data will help to deeply understand the mechanism of the biological activities. In recent years, various computational methods have been developed for identifying protein complexes from PPI networks. Almost all the basic computational methods mainly depend on the association of topological analysis of PPI networks. However, most of them fail to satisfactorily capture the global and local topological structures of the PPI networks, as well as the diversity of connectivity patterns between individual nodes at the same time. To solve this problem, in this work we propose a node embedding based alias sampling extension method to detect protein complexes. More specifically, for a given set of seed nodes, it first uses the alias sampling strategy based on protein node embedding similarities to select potential addable nodes. Then it makes use of a new conductance measure, which could better quantify the likelihood of a subgraph being a protein complex, to decide whether to extend the current candidate subgraph in order to find protein complexes. Evaluated on six real yeast PPI networks, our method outperforms state-of-the-art methods in detecting protein complexes. Furthermore, the experimental results demonstrate the protein complexes predicted by our method have higher biological significance.

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GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures

Cited by 56 publications

References 34 publications

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

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

Drug-Drug Interaction Prediction on a Biomedical Literature Knowledge Graph

Using Alias Sampling Strategy Based on Network Embeddings to Detect Protein Complexes

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