SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization

Yu, Yue; Huang, Kexin; Zhang, Chao; Glass, Lucas M.; Sun, Jimeng; Xiao, Cao

doi:10.1093/bioinformatics/btab207

Cited by 90 publications

(73 citation statements)

References 31 publications

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“…With the increasing availability of large biomedical knowledge graphs (KGs), some studies attempt to incorporate KG with other data (i.e., drug molecular structures) for multi-type DDI predictions via graph neural networks (GNNs) [ 26 , 27 ]. However, there are data redundancy and noise in the large KGs, in which only a small subgraph is relevant to a prediction target [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Lin

Chen

et al. 2022

J Cheminform

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The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL.

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Section: Introductionmentioning

confidence: 99%

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Lin

Chen

et al. 2022

J Cheminform

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“…Feng et al [ 14 ] predicted DDI using Graph Convolutional Network (GCN) and DNN. In addition, there are also many methods about multi-type DDI prediction [ 15 – 17 ]. Nyamabo et al [ 18 ] proposed to predict DDIs by the interactions between drug substructures.…”

Section: Introductionmentioning

confidence: 99%

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction

Liu

et al. 2022

BMC Bioinformatics

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Background Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. Results In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. Conclusions Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.

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“…Our goal is to find the target answer entity e a for the specific query. KG reasoning is attracting growing interest, and it has been widely applied in recommendation [4,32], question answering [14] and drug interaction prediction [39].…”

Section: Introductionmentioning

confidence: 99%

Learning Adaptive Propagation for Knowledge Graph Reasoning

Zhang¹,

Zhou²,

Yao³

et al. 2022

Preprint

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Due to the success of Graph Neural Networks (GNNs) in learning from graphstructured data, various GNN-based methods have been introduced to learn from knowledge graphs (KGs). In this paper, to reveal the key factors underneath existing GNN-based methods, we revisit exemplar works from the lens of the propagation path. We find that the answer entity can be close to queried one, but the information dependency can be long. Thus, better reasoning performance can be obtained by exploring longer propagation paths. However, identifying such a long-range dependency in KG is hard since the number of involved entities grows exponentially. This motivates us to learn an adaptive propagation path that filters out irrelevant entities while preserving promising targets during the propagation. First, we design an incremental sampling mechanism where the close and promising target can be preserved. Second, we design a learning-based sampling distribution to identify the targets with fewer involved entities. In this way, GNN can go deeper to capture long-range information. Extensive experiments show that our method is efficient and achieves state-of-the-art performances in both transductive and inductive reasoning settings, benefiting from the deeper propagation. 1Preprint. Under review.

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SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization

Cited by 90 publications

References 31 publications

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction

Learning Adaptive Propagation for Knowledge Graph Reasoning

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