GIMDA: Graphlet interaction‐based MiRNA‐disease association prediction

Chen, Xing; Guan, Na-Na; Li, Jianqiang; Ge, Yan

doi:10.1111/jcmm.13429

Cited by 24 publications

(21 citation statements)

References 84 publications

(117 reference statements)

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“…From above formula 3, it is easy to see that the diseases in the same layer of DAG(D) will make the same contribution to the semantic value of D. Moreover, for diseases in the same layer of DAG(D), it is reasonable to assume that the diseases appeared in less DAGs will be more specific than those diseases appeared in more DAGs (Chen et al, 2018a). Hence, in order to protrude the contribution of these more specific diseases, the contribution of the node d in T(D) to the semantic value of the disease D could be obtained according to the following formula as well (Chen et al, 2015):…”

Section: Disease Semantic Similarity Model Imentioning

confidence: 99%

BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction

et al. 2020

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Recent studies have indicated that microRNAs (miRNAs) are closely related to sundry human sophisticated diseases. According to the surmise that functionally similar miRNAs are more likely associated with phenotypically similar diseases, researchers have proposed a variety of valid computational models through integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity to discover the potential miRNA-disease relationships in biomedical researches. Taking account of the limitations of previous computational models, a new computational model based on biased heat conduction for MiRNA-Disease Association prediction (BHCMDA) was proposed in this paper, which can achieve the AUC of 0.8890 in LOOCV (Leave-One-Out Cross Validation) and the mean AUC of 0.9060, 0.8931 under the framework of twofold cross validation, fivefold cross validation, respectively. In addition, BHCMDA was further implemented to the case studies of three vital human cancers, and simulation results illustrated that there were 88% (Esophageal Neoplasms), 92% (Colonic Neoplasms) and 92% (Lymphoma) out of top 50 predicted miRNAs having been confirmed by experimental literatures, separately, which demonstrated the good performance of BHCMDA as well. Thence, BHCMDA would be a useful calculative resource for potential miRNA-disease association prediction.

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Section: Disease Semantic Similarity Model Imentioning

confidence: 99%

BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction

et al. 2020

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“…Chen et al devised a method GIMDA based on graphlet interaction which was applied to analyse the relevance between two points. 28 The AUCs of GIMDA in global, local LOOCV and 5-fold cross validation turned out to be 0.9006 and 0.8455 and 0.8927 respectively. However, as NTSMDA and GIMDA strongly depends on network topological structure, they cannot be applied to diseases without any known associated miR- work distance to predict miRNA-disease associations.…”

mentioning

confidence: 93%

“…Chen et al. devised a method GIMDA based on graphlet interaction which was applied to analyse the relevance between two points . The AUCs of GIMDA in global, local LOOCV and 5‐fold cross validation turned out to be 0.9006 and 0.8455 and 0.8927 respectively.…”

Section: Introductionmentioning

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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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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“…Experimental results demonstrate that LRSSLMDA is a valuable computational model. In addition, many previous methods are based on machine learning algorithms [29, 30], matrix completion [31–33] and graph theory [34]. For example, Shen et al [35] proposed CMFMDA that uses WKNKN to estimate association probability for unknown associations between miRNA and disease, and uses Collaborative Matrix Factorization to uncover the potential association.…”

Section: Introductionmentioning

confidence: 99%

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association

et al. 2018

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BackgroundIn the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations.ResultFirst, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs.ConclusionsOur proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5273-x) contains supplementary material, which is available to authorized users.

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GIMDA: Graphlet interaction‐based MiRNA‐disease association prediction

Cited by 24 publications

References 84 publications

BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction

BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction

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

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association

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