scholarly journals iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm

2020 ◽  
Vol 18 ◽  
pp. 2391-2400
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Zhen-Hao Guo
Keyword(s):  
Biological Network ◽  
Graph Embedding ◽  
Disease Associations

scholarly journals Predicting disease associations via biological network analysis

2014 ◽  
Vol 15 (1) ◽  
pp. 304 ◽  
Author(s):  
Kai Sun ◽  
Joana P Gonçalves ◽  
Chris Larminie ◽  
Nataša Pržulj
Keyword(s):  
Network Analysis ◽  
Biological Network ◽  
Biological Network Analysis ◽  
Disease Associations

Graph embedding and ensemble learning for predicting gene-disease associations

2020 ◽  
Vol 23 (4) ◽  
pp. 360
Author(s):  
Haorui Wang ◽  
Xiaochan Wang ◽  
Zhouxin Yu ◽  
Wen Zhang
Keyword(s):  
Ensemble Learning ◽  
Graph Embedding ◽  
Disease Associations

Graph embedding and ensemble learning for predicting gene-disease associations

2020 ◽  
Vol 23 (4) ◽  
pp. 360
Author(s):  
Zhouxin Yu ◽  
Wen Zhang ◽  
Haorui Wang ◽  
Xiaochan Wang
Keyword(s):  
Ensemble Learning ◽  
Graph Embedding ◽  
Disease Associations

Predicting circRNA-disease associations based on autoencoder and graph embedding

2021 ◽  
Author(s):  
Jing Yang ◽  
Xiujuan Lei
Keyword(s):  
Graph Embedding ◽  
Disease Associations

scholarly journals iMDA-BN: Identification of miRNA-Disease Associations based on the Biological Network and Graph Embedding Algorithm

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Domain Knowledge ◽  
Biological Network ◽  
Representation Learning ◽  
Biological Properties ◽  
Disease Association ◽  
Sequence Information ◽  
Network Representation ◽  
Disease Associations

AbstractBenefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.

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

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Lei Zhang ◽  
Bailong Liu ◽  
Zhengwei Li ◽  
Xiaoyan Zhu ◽  
Zhizhen Liang ◽  
...  
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Complex Structure ◽  
Graph Embedding ◽  
Prostate Neoplasms ◽  
Prediction Performance ◽  
Linear Transformations ◽  
Disease Associations ◽  
Meta Path ◽  
Leave One Out

Abstract 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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