MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

Fu, Xinyu; Zhang, Jiani; Meng, Ziqiao; King, Irwin

doi:10.1145/3366423.3380297

Cited by 499 publications

(349 citation statements)

References 16 publications

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“…We collected 5430 experimentally supported miRNA-disease associations from HMDD V2.0 [33] to act as the data set in our prediction task. Then, we employed global LOOCV and fivefold cross validation strategies on the experimental data.…”

Section: Experimental Approaches and Evaluation Criteriamentioning

confidence: 99%

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Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

et al. 2020

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Background Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed miRNA-disease associations. Thus, computational models predicting unidentified miRNA-disease pairs in an efficient way are becoming promising research topics. Although existing methods have performed well to reveal unidentified miRNA-disease associations, more work is still needed to improve prediction performance. Results In this work, we present a novel multiple meta-paths fusion graph embedding model to predict unidentified miRNA-disease associations (M2GMDA). Our method takes full advantage of the complex structure and rich semantic information of miRNA-disease interactions in a self-learning way. First, a miRNA-disease heterogeneous network was derived from verified miRNA-disease pairs, miRNA similarity and disease similarity. All meta-path instances connecting miRNAs with diseases were extracted to describe intrinsic information about miRNA-disease interactions. Then, we developed a graph embedding model to predict miRNA-disease associations. The model is composed of linear transformations of miRNAs and diseases, the means encoder of a single meta-path instance, the attention-aware encoder of meta-path type and attention-aware multiple meta-path fusion. We innovatively integrated meta-path instances, meta-path based neighbours, intermediate nodes in meta-paths and more information to strengthen the prediction in our model. In particular, distinct contributions of different meta-path instances and meta-path types were combined with attention mechanisms. The data sets and source code that support the findings of this study are available at https://github.com/dangdangzhang/M2GMDA. Conclusions M2GMDA achieved AUCs of 0.9323 and 0.9182 in global leave-one-out cross validation and fivefold cross validation with HDMM V2.0. The results showed that our method outperforms other prediction methods. Three kinds of case studies with lung neoplasms, breast neoplasms, prostate neoplasms, pancreatic neoplasms, lymphoma and colorectal neoplasms demonstrated that 47, 50, 49, 48, 50 and 50 out of the top 50 candidate miRNAs predicted by M2GMDA were validated by biological experiments. Therefore, it further confirms the prediction performance of our method.

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Section: Experimental Approaches and Evaluation Criteriamentioning

confidence: 99%

“…Meta-paths can be applied to explore the structure information and capture the rich semantic information in heterogeneous networks [33]. Zhang et al [34] used meta-paths to directly extract features from miRNA-disease interactions.…”

Section: Introductionmentioning

confidence: 99%

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

et al. 2020

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“…The FB15k-237 has 237 different kinds of relations, on the other hand, Symptoms-in-Chinese has more entities with a larger knowledge graph. We use a subset of IMDb containing 4278 movies, 2081 directors and 5257 actors following [7]. Movies are assigned to one of three classes (Action, Comedy, and Drama).…”

Section: Datasetsmentioning

confidence: 99%

“…We conduct experiments on the IMDb and Symptoms-in-Chinese datasets to compare the performance of different models on the node classification task. We feed the trained embedding of labeled nodes generated by each model to a linear support vector machine (SVM) classifier with varying training proportions following [7]. The train/test splits for the linear SVM are the same across embedding models.…”

Section: Node Classification (Rq2)mentioning

confidence: 99%

Multi-modal Multi-relational Feature Aggregation Network for Medical Knowledge Representation Learning

Zhang

Fang

Qian

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Representation learning of medical Knowledge Graph (KG) is an important task and forms the fundamental process for intelligent medical applications such as disease diagnosis and healthcare question answering. Therefore, many embedding models have been proposed to learn vector presentations for entities and relations but they ignore three important properties of medical KG: multimodal, unbalanced and heterogeneous. Entities in the medical KG can carry unstructured multi-modal content, such as image and text. At the same time, the knowledge graph consists of multiple types of entities and relations, and each entity has various number of neighbors. In this paper, we propose a Multi-modal Multi-Relational Feature Aggregation Network (MMRFAN) for medical knowledge representation learning. To deal with the multi-modal content of the entity, we propose an adversarial feature learning model to map the textual and image information of the entity into the same vector space and learn the multi-modal common representation. To better capture the complex structure and rich semantics, we design a sampling mechanism and aggregate the neighbors with intra and interrelation attention. We evaluate our model on three knowledge graphs, including FB15k-237, IMDb and Symptoms-in-Chinese with link prediction and node classification tasks. Experimental results show that our approach outperforms state-of-the-art method. CCS CONCEPTS • Computing methodologies → Knowledge representation and reasoning.

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“…Before aggregating the information from different heterogeneous graph based on above three view for each entity, we fist should get node embedding in each heterogeneous graph according to the study [24]. In each heterogeneous graph, we should notice that the meta-path [25] based neighbors of each node play a different role and show different importance in learning node embedding for the specific task [26]. Graph convolutional network [27] is a multi-layer neural network that operates directly on graph data and induces the embedding vectors of nodes based on the properties of their neighborhoods.…”

Section: Heterogeneous Graph Embeddingmentioning

confidence: 99%

Relation Extraction for Chinese Clinical Records Using Multi-View Graph Learning

Ruan

Luo

et al. 2020

IEEE Access

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MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

Cited by 499 publications

References 16 publications

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

Multi-modal Multi-relational Feature Aggregation Network for Medical Knowledge Representation Learning

Relation Extraction for Chinese Clinical Records Using Multi-View Graph Learning

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