NDAMDA: Network distance analysis for Mi<scp>RNA</scp>‐disease association prediction

Chen, Xing; Wang, Le‐Yi; Huang, Li

doi:10.1111/jcmm.13583

Cited by 32 publications

(17 citation statements)

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“…By performing a depth-first search algorithm on the heterogeneous network to infer disease-related miRNAs, You et al (2017) presented a model called PBMDA, which could be employed in new diseases or miRNAs, greatly improving practicability and reliability. Chen et al (2018c) designed a Network Distance Analysis method for miRNA-disease Association prediction (NDAMDA), which used the direct network distance and average network distances between two miRNAs or diseases. However, this model might cause a bias toward miRNAs with more known related diseases and might not be applicable to the diseases where associated miRNAs tend to be randomly distributed in the network.…”

Section: Introductionmentioning

confidence: 99%

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

Wang

Gao

et al. 2020

Front. Genet.

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Accumulating biological and clinical evidence has confirmed the important associations between microRNAs (miRNAs) and a variety of human diseases. Predicting disease-related miRNAs is beneficial for understanding the molecular mechanisms of pathological conditions at the miRNA level, and facilitating the finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. However, the challenge for researchers is to establish methods that can effectively combine different datasets and make reliable predictions. In this work, we propose the method of Multi-Similarity based Combinative Hypergraph Learning for Predicting MiRNA-disease Association (MSCHLMDA). To establish this method, complex features were extracted by two measures for each miRNA-disease pair. Then, K-nearest neighbor (KNN) and K-means algorithm were used to construct two different hypergraphs. Finally, results from combinative hypergraph learning were used for predicting miRNA-disease association. In order to evaluate the prediction performance of our method, leave-one-out cross validation and 5-fold cross validation was implemented, showing that our method had significantly improved prediction performance compared to previously used methods. Moreover, three case studies on different human complex diseases were performed, which further demonstrated the predictive performance of MSCHLMDA. It is anticipated that MSCHLMDA would become an excellent complement to the biomedical research field in the future.

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Section: Introductionmentioning

confidence: 99%

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

Wang

Gao

et al. 2020

Front. Genet.

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“…Le et al 37 applied RWR, PRINCE, PRP and KSM to correlation analysis for predicting miRNA-disease associations. Chen et al 38 used network distance analysis. Yu et al 39 used global linear neighbours to predict miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

et al. 2019

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“…proposed NDAMDA based on network distance to predict miRNA‐disease associations. NDAMDA not only considered the direct network distance between two miRNAs or diseases but also took their respective mean network distances to all other miRNAs or diseases into account . The reliable performance of NDAMDA was certified by the AUCs of 0.8920, 0.8062 and 0.8935 obtained in global LOOCV, local LOOCV and 5‐fold cross validation 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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NDAMDA: Network distance analysis for MiRNA‐disease association prediction

Cited by 32 publications

References 50 publications

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

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

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