RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

Chen, Xing; Wu, Qiaofeng; Ge, Yan

doi:10.1080/15476286.2017.1312226

Cited by 141 publications

(101 citation statements)

References 71 publications

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“…These diseases were selected in our case studies because they are the most common cancer types, with high incidence and death rate each year. In addition, they have been used as case studies in many previous publications . Unlike cross‐validations that solely depended on HMDD v2.0, our case studies used HMDD v2.0 as the training database for KFRLSMDA and dbDEMC and miR2Disease as the validation databases for confirming the predicted potential associations.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, they have been used as case studies in many previous publications. 22,27,30,33,40,41,43 Unlike cross-validations that solely depended on HMDD v2.0, our case studies used HMDD v2.0 as the training database for KFRLSMDA and dbDEMC 44 and miR2Disease 45 as the validation databases for confirming the predicted potential associations. The following is the basic information about dbDEMC and miR2Disease.…”

Section: Case Studiesmentioning

confidence: 99%

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In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan

Wang

Zhang

et al. 2019

J Cellular Molecular Medi

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Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion‐based Regularized Least Squares for MiRNA‐Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA‐disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave‐one‐out cross‐validation (LOOCV) and 5‐fold cross‐validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5‐fold cross‐validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

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Section: Resultsmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan

Wang

Zhang

et al. 2019

J Cellular Molecular Medi

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“…Chen et al proposed another computational model called RKNNMDA which utilized the SVM ranking model to obtain reliable k -nearest-neighbors for each miRNA and disease. Specifically, it can be used to predict potential miRNAs for diseases without any known miRNAs[28]. They further proposed another model named MKRMDA to discover the potential miRNA-disease associations[29].…”

Section: Introductionmentioning

confidence: 99%

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Liang

Xiao

et al. 2018

RNA Biology

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Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have 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 global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

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“…Based on these biological datasets and efficient calculation method, miREFRWR could be an effective tool in computational biology. What is more, Chen et al [52] also proposed a computational model RKNNMDA to predict the potential associations between miRNA and disease. Four biological datasets, experimentally verified human miRNAdisease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases were integrated into RKNNMDA.…”

Section: Introductionmentioning

confidence: 99%

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

et al. 2017

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As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

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RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

Cited by 141 publications

References 71 publications

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

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