A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks

Li, Chunyan; Liu, Hongju; Hu, Qian; Que, Jinlong; Yao, Junfeng

doi:10.3390/cells8090977

Cited by 67 publications

(17 citation statements)

References 47 publications

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“…In CV-Triple, we randomly divided all MD-T instances into 10 equal parts, one part reserved as a test set, and the others form the training set. Due to the lack of MD-T negative samples provided by biologists, we constructed a set

of negative samples from the unconfirmed or non-existent MD-T by a strategy similar to [ 33 , 34 ]. For relation type r , we calculated an average representation feature

of all positive samples in the training set.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction

Han

et al. 2021

Biomedicines

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Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It is even more important to reveal associations for currently unassociated miRNAs and diseases. Methods have been recently proposed to make predictions on the association types of miRNA-disease pairs through restricted Boltzman machines, label propagation theories and tensor completion algorithms. None of them has exploited the non-linear characteristics in the miRNA-disease association network to improve the performance. We propose to use attributed multi-layer heterogeneous network embedding to learn the latent representations of miRNAs and diseases from each association type and then to predict the existence of the association type for all the miRNA-disease pairs. The performance of our method is compared with two newest methods via 10-fold cross-validation on the database HMDD v3.2 to demonstrate the superior prediction achieved by our method under different settings. Moreover, our real predictions made beyond the HMDD database can be all validated by NCBI literatures, confirming that our method is capable of accurately predicting new associations of miRNAs with diseases and their association types as well.

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of negative samples from the unconfirmed or non-existent MD-T by a strategy similar to [ 33 , 34 ]. For relation type r , we calculated an average representation feature

of all positive samples in the training set.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction

Han

et al. 2021

Biomedicines

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“…Based on the similarity method, the association prediction between RNAs and diseases can also integrate more useful information. Li C. et al (2019) integrated miRNA–disease, miRNA–gene, disease–gene, and PPI networks. Furthermore, based on the information extracted by GCN, the top 10 unknown interactions between miRNAs and diseases were analyzed.…”

Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

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“…Recently, some novel AI techniques in Representation Learning and Graph Neural Networks, for example, the widely used model Node2vec, have been proposed and investigated thoroughly [22]- [25]. Implemented with that, many strategies have already been adopted and obtained success in the research of disease analysis [26], medical imaging [27], [28]. In this sense, introducing AI into TCM research has attracted extreme attention as well, and hereby, has resulted in many achievements [29]- [33].…”

Section: Introductionmentioning

confidence: 99%

Approaching High-Accuracy Side Effect Prediction of Traditional Chinese Medicine Compound Prescription Using Network Embedding and Deep Learning

Wang

Yan

et al. 2020

IEEE Access

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In this paper, we realize high-accuracy side-effect prediction of Traditional Chinese Medicine Compound Prescription by introducing network embedding and deep learning. A random walk network that could efficiently interpret the information in the prescription is established from a conventional Bag-of-Word network. After the validation of this random walk network, the highest prediction accuracy reaches 0.908 where a simple five-layer artificial neural network is implemented, rendering this method is promising for Traditional Chinese Medicine side-effect prediction and other medicines with a similar structure such as the compound drugs. INDEX TERMS Side effect, network embedding, deep learning, traditional Chinese medicine.

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A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks

Cited by 67 publications

References 47 publications

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction

Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction

Graph Neural Networks and Their Current Applications in Bioinformatics

Approaching High-Accuracy Side Effect Prediction of Traditional Chinese Medicine Compound Prescription Using Network Embedding and Deep Learning

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