scholarly journals GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm

2020 ◽  
Vol 16 (5) ◽  
pp. e1007568 ◽  
Author(s):  
Lei Wang ◽  
Zhu-Hong You ◽  
Yang-Ming Li ◽  
Kai Zheng ◽  
Yu-An Huang
Keyword(s):  
New Method ◽  
Convolutional Network ◽  
Network Algorithm ◽  
Disease Associations

scholarly journals Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1012 ◽  
Author(s):  
Xuan ◽  
Pan ◽  
Zhang ◽  
Liu ◽  
Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Heterogeneous Network ◽  
Heterogeneous Data ◽  
Superior Performance ◽  
Convolutional Network ◽  
Topological Information ◽  
Disease Pair ◽  
Disease Associations ◽  
The Right

Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

scholarly journals EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shanchen Pang ◽  
Yu Zhuang ◽  
Xinzeng Wang ◽  
Fuyu Wang ◽  
Sibo Qiao
Keyword(s):  
Information Aggregation ◽  
Disease Association ◽  
Supplementary Information ◽  
Graph Structure ◽  
Association Network ◽  
Convolutional Network ◽  
Edge Information ◽  
Biological Studies ◽  
Convolution Model ◽  
Disease Associations

Abstract Background A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA-disease associations could provide us a root cause understanding of the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time-consuming and costly. Therefore, we come up with an efficient models to solve this challenge. Results In this work, we propose a deep learning model called EOESGC to predict potential miRNA-disease associations based on embedding of embedding and simplified convolutional network. Firstly, integrated disease similarity, integrated miRNA similarity, and miRNA-disease association network are used to construct a coupled heterogeneous graph, and the edges with low similarity are removed to simplify the graph structure and ensure the effectiveness of edges. Secondly, the Embedding of embedding model (EOE) is used to learn edge information in the coupled heterogeneous graph. The training rule of the model is that the associated nodes are close to each other and the unassociated nodes are far away from each other. Based on this rule, edge information learned is added into node embedding as supplementary information to enrich node information. Then, node embedding of EOE model training as a new feature of miRNA and disease, and information aggregation is performed by simplified graph convolution model, in which each level of convolution can aggregate multi-hop neighbor information. In this step, we only use the miRNA-disease association network to further simplify the graph structure, thus reducing the computational complexity. Finally, feature embeddings of both miRNA and disease are spliced into the MLP for prediction. On the EOESGC evaluation part, the AUC, AUPR, and F1-score of our model are 0.9658, 0.8543 and 0.8644 by 5-fold cross-validation respectively. Compared with the latest published models, our model shows better results. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA-disease associations.

scholarly journals Inferring disease-associated microRNAs using semi-supervised multi-label graph convolutional networks

2019 ◽  
Author(s):  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Expression Profiles ◽  
Tissue Expression ◽  
Computational Method ◽  
Interaction Networks ◽  
Convolutional Network ◽  
Protein Coding ◽  
Convolutional Networks ◽  
Disease Associations ◽  
Supervised Methods ◽  
Inflammatory Bowel

AbstractMicroRNAs (miRNAs) play crucial roles in many biological processes involved in diseases. The associations between diseases and protein coding genes (PCGs) have been well investigated, and further the miRNAs interact with PCGs to trigger them to be functional. Thus, it is imperative to computationally infer disease-miRNA associations under the context of interaction networks.In this study, we present a computational method, DimiG, to infer miRNA-associated diseases using semi-supervised Graph Convolutional Network model (GCN). DimiG is a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations and tissue expression profiles. DimiG is trained on disease-PCG associations and a graph constructed from interaction networks of PCG-PCG and miRNA-PCG using semi-supervised GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set collected from verified disease-miRNA associations. Our results demonstrate that the new DimiG yields promising performance and outperforms the best published baseline method not trained on disease-miRNA associations by 11% and is also superior to two state-of-the-art supervised methods trained on disease-miRNA associations. Three case studies of prostate cancer, lung cancer and Inflammatory bowel disease further demonstrate the efficacy of DimiG, where the top miRNAs predicted by DimiG for them are supported by literature or databases.

scholarly journals EOESGC: Predicting miRNA−disease Associations Based on Embedding of Embedding and Simplified Graph Convolutional Network

2021 ◽  
Author(s):  
Shanchen Pang ◽  
yu Zhuang ◽  
Xinzeng Wang ◽  
Fuyu Wang ◽  
Sibo Qiao
Keyword(s):  
Positional Information ◽  
Disease Association ◽  
Feature Representation ◽  
Experimental Results ◽  
Graph Structure ◽  
Convolutional Network ◽  
Structure Information ◽  
Biological Studies ◽  
Disease Associations ◽  
Computational Difficulty

Abstract Background: A large number of biological studies have shown that miRNAs are inextricably linked to many complex diseases. Studying the miRNA−disease associations could provide us a root cause understanding on the underlying pathogenesis in which promotes the progress of drug development. However, traditional biological experiments are very time consuming and costly. Therefore, we come up with more efficient models to solve this challenge. Results: In this work, we propose a deep learning model called EOESGC to predict potential miRNA−disease associations based on embedding of embedding and simplified convolutional network. Firstly, a coupled heterogeneous graph is constructed by using the integrated disease similarity, integrated miRNA similarity and miRNA−disease association networks where parts of the connected edges with less similarity values are removed to simplify the graph structure. The initial feature representation of nodes in the graph is learned using the embedding of embedding model(EOE) based on the principle that the nodes with associations are close to each other and the nodes without association are far from each other. The use of EOE can effectively learn the positional information among nodes and protect the graph structure information to some extent. Then the initial features of the nodes are fed into the simplified graph convolutional network(SGC), and in this step we only use miRNA−disease association network to further simplify the graph structure and thus reduce the computational complexity. Finally, feature embeddings of both miRNA and disease spliced into the MLP for prediction. The two graph simplifications of our model effectively reduce the computational difficulty, and the experimental results show that our model can indeed predict the potential miRNA−disease associations effectively. Compared with the latest published models, our model shows better results. On EOESGC evaluation part, the AUC, AUPR and F1 of our model are 0.9658, 0.8543 and 0.8644 by 5−fold cross validation respectively. In addition, we predict the top 20 potential miRNAs for breast cancer and lung cancer, most of which are validated in the dbDEMC and HMDD3.2 databases. Conclusion: The comprehensive experimental results show that EOESGC can effectively identify the potential miRNA−disease associations.

GCNSDA: Predicting snoRNA-disease associations via graph convolutional network

2021 ◽  
Author(s):  
Dayun Liu ◽  
Yi Luo ◽  
Jingjing Zheng ◽  
Hanlin Xu ◽  
Jiaxuan Zhang ◽  
...  
Keyword(s):  
Convolutional Network ◽  
Disease Associations

NMCMDA: neural multicategory MiRNA–disease association prediction

2021 ◽  
Author(s):  
Jingru Wang ◽  
Jin Li ◽  
Kun Yue ◽  
Li Wang ◽  
Yuyun Ma ◽  
...  
Keyword(s):  
Cost Effective ◽  
Underlying Mechanism ◽  
Disease Association ◽  
Disease Category ◽  
Convolutional Network ◽  
Time Saving ◽  
Disease Associations ◽  
Multiple Category ◽  
Latent Representations ◽  
Main Components

Abstract Motivation There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA–disease associations are time-saving and cost-effective that are highly desired for us. Results We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA–disease association prediction (NMCMDA) for predicting multiple-category miRNA–disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA–disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA–disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA–disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

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