KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations

Jiang, Changzhi; Tang, Minli; Jin, Shilei; Huang, Wei; Liu, Xiangrong

doi:10.1109/tcbb.2022.3184362

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…• KGNMDA [48] represent the relations between microbes and diseases based on the Gaussian kernel and then learn the similarity between them in an uncertain manner. They then use a linear transformation to predict the scores across microbe-disease relations.…”

Section: ) Baselinesmentioning

confidence: 99%

Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning

Hoang,

Nguyen,

Lee

et al. 2023

IEEE Access

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Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. However, learning representations to capture entities and relations with literal information in KGs is challenging as the KGs show heterogeneous properties and various types of literal information. Meanwhile, the existing methods mostly aim to preserve graph structures surrounding target nodes without considering different types of literals, which could also carry significant information. In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG. Specifically, we construct a knowledge graph that is built from EMRs along with literal information collected from various animal hospitals. We then fuse different types of entities and node feature information into unified vector representations through gate networks. Finally, we propose a self-supervised learning task to learn graph structure in pretext tasks and then towards various downstream tasks. Experimental results on link prediction tasks demonstrate that our model outperforms the baselines that consist of state-of-the-art models.

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Section: ) Baselinesmentioning

confidence: 99%

Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning

Hoang,

Nguyen,

Lee

et al. 2023

IEEE Access

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“…Most existing approaches consider only a single relation-type even ignore the connection, fail to cover the intricate connection among the gut microbes of different hosts. Graph learning [33,35,36] are well-suited for handling graph-structured data with rich relationships [30,3], and can represent information at various depths. Therefore, we utilize Graph Neural Networks (GNNs) to learn the connections among gut microbes from different hosts, effectively guiding disease prediction.…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment

Chen

Liu

et al. 2023

World Wide Web

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Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.

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“…Machine learning-based algorithms take MDA prediction as a classification problem. For example, to discover potential MDAs, BPNNHMDA (Li et al, 2020 ) adopted a neural network structure, GATMDA (Long et al, 2021 ) exploited a graph attention network with inductive matrix completion, DMFMDA (Liu et al, 2020 ) utilized a deep neural network-based deep matrix factorization model, NinimHMDA (Ma and Jiang, 2020 ) explored an end-to-end graph convolutional neural network structure, KGNMDA (Jiang et al, 2022 ) used a graph neural network model, MGATMDA (Liu et al, 2021 ) comprised decomposer, combiner, and predictor where the decomposer captured the latent components using node-level attention mechanism, the combiner obtained unified embedding using component-level attention mechanism, and unknown microbe–disease pairs were classified by a fully connected network. HNGFL (Wang et al, 2022 ) designed an embedding algorithm for feature learning and used support vector machine for MDA classification.…”

Section: Introductionmentioning

confidence: 99%

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Peng,

Huang,

Tian

et al. 2023

Front. Microbiol.

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BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

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KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations

Cited by 10 publications

References 34 publications

Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning

Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning

Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

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