<scp>SNMDA</scp>: A novel method for predicting micro<scp>RNA</scp>‐disease associations based on sparse neighbourhood

Qu, Yu; Zhang, Huaxiang; Liang, Cheng; Ding, Pingjian; Luo, Jiawei

doi:10.1111/jcmm.13799

Cited by 11 publications

(5 citation statements)

References 59 publications

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“…We further compared our method with four state‐of‐the‐art methods, that is, SNMDA, HGIMDA, EGBMMDA and MKRMDA. It is worth mentioning that SNMDA was also proposed by our team and achieved superior results . Moreover, in order to clearly demonstrate the power of our method, we removed the similarity matrices constructed by matrix completion for both miRNAs and diseases and compared its prediction performance with MCLPMDA in all cross‐validation frameworks.…”

Section: Resultsmentioning

confidence: 96%

“…It is worth mentioning that SNMDA was also proposed by our team and achieved superior results. 48 Moreover, in order to clearly demonstrate the power of our method, we removed the similarity matrices constructed by matrix completion for both miRNAs and diseases and compared its prediction perfor- Next, we adopted another evaluation metric called leave one disease out cross validation (LODOCV) to test the ability of our method to predict for diseases without any known associated miRNAs.…”

Section: Performance Evaluationmentioning

confidence: 99%

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MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Liang

Xiao

et al. 2018

J Cellular Molecular Medi

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MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.

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Section: Resultsmentioning

confidence: 96%

Section: Performance Evaluationmentioning

confidence: 99%

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Liang

Xiao

et al. 2018

J Cellular Molecular Medi

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“…Predicting microRNA-disease associations based on sparse neighborhoods (SNMDA) : Qu et al ( 2018a ) presented a method named SNMDA that takes advantage of the sparsity of the miRNA-disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

Zhu

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex. The computational network should consider the local and global influence of neighborhoods from the network. Furthermore, predicting disease-related miRNAs without any known associations is also very important. This study presents a new computational method that constructs a heterogeneous network composed of a miRNA similarity network, disease similarity network, and known miRNA-disease association network. The miRNA similarity considers the miRNAs and their possible families and clusters. The information of each node in heterogeneous network is obtained by aggregating neighborhood information with graph convolutional networks (GCNs), which can pass the information of a node to its intermediate and distant neighbors. Disease-related miRNAs with no known associations can be predicted with the reconstructed heterogeneous matrix. We apply 5-fold cross-validation, leave-one-disease-out cross-validation, and global and local leave-one-out cross-validation to evaluate our method. The corresponding areas under the curves (AUCs) are 0.9616, 0.9946, 0.9656, and 0.9532, confirming that our approach significantly outperforms the state-of-the-art methods. Case studies show that this approach can effectively predict new diseases without any known miRNAs.

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“…The network or graph algorithms focused on constructing miRNAs and/or disease similarity networks and efficient transferring miRNA-disease association labels between similar miRNAs and/or similar diseases in the network. Therefore, label propagation algorithm, which has the advantages of simplicity and efficiency on the miRNA/disease similarity networks, often constitutes the core component of the algorithm framework for this type of methods, e.g., MCLPMDA [15], LPLNS [16], SNMDA [17], and HLPMDA [18]. Nevertheless, more sophisticated algorithm designs are often crucial for successful prediction of miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

Benchmark of computational methods for predicting microRNA-disease associations

et al. 2019

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Background A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness. Results Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations. Conclusion Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.

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SNMDA: A novel method for predicting microRNA‐disease associations based on sparse neighbourhood

Cited by 11 publications

References 59 publications

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

Benchmark of computational methods for predicting microRNA-disease associations

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