Meta-path Based MiRNA-Disease Association Prediction

Lv, Hao; Jin, Li; Zhang, Sai; Yue, Kun; Wei, Shaoyu

doi:10.1007/978-3-030-18590-9_3

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Zhang et al [ 29 ] constructed a multiple meta-path fusion graph embedding model through integrating nodes and edge information to predict miRNA–disease associations. Lv et al [ 30 ] predicted disease-associated miRNAs through solving a meta-path in a heterogeneous network composed of miRNA similarity, diseases similarity, and miRNA–disease associations. However, this method failed to solve the problems on parameter selection.…”

Section: Introductionmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA–disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA–disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease–miRNA association prediction model.

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Section: Introductionmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Chu

Wang

Dai

et al. 2021

Briefings in Bioinformatics

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Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

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Metapath-aggregated multilevel graph embedding for miRNA‒disease association prediction

Cao,

Chen,

Yang

et al. 2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Meta-path Based MiRNA-Disease Association Prediction

Cited by 5 publications

References 24 publications

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Metapath-aggregated multilevel graph embedding for miRNA‒disease association prediction

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