An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy

Wang, Chun-Chun; Chen, Xing; Yin, Jun; Qu, Jia

doi:10.1080/15476286.2019.1568820

Cited by 35 publications

(25 citation statements)

References 51 publications

(55 reference statements)

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“…To our knowledge, BNPMDA (Chen et al, 2018h), MDHGI (Chen et al, 2018i), NSEMDA (Wang C.-C. et al, 2019), RFMDA (Chen et al, 2018f), and SNMFMDA (Zhao et al, 2018) are the most advanced prediction methods in inferring miRNA-disease associations so far. Due to the fact that the databases used by these five methods are similar with that of MSFSP, we compared MSFSP with these five methods on the predictive performance.…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

“…Inspired by the successful application of machine learning methods in the field of bioinformatics, many researchers used supervised machine learning methods to predict a miRNAdisease association (Chen et al, 2015a(Chen et al, ,b, 2017a(Chen et al, , 2018d(Chen et al, ,f, 2019aLuo et al, 2017a;Xuan et al, 2018Xuan et al, , 2019bWang C.-C. et al, 2019;Wang L. et al, 2019;Zhang L. et al, 2019;Zhao et al, 2019), but which need negative samples for training. Because it is hard to obtain the experimentally verified less-known miRNA-disease associations and negative samples, some semi-supervised learning approaches (such as regularized least squares) with remarkable prediction results were proposed (Chen and Huang, 2017;Chen et al, 2017cPeng et al, 2017b;Xu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

et al. 2020

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Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

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Section: Comparison To Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

et al. 2020

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“…With the development of machine learning algorithms, many researchers have begun to use this technology to solve various biological problems, such as prediction of drug-target interactions (Chen et al, 2016d), synergistic drug combinations (Chen et al, 2016a), disease related long non-coding RNAs (Chen et al, 2017c), miRNA-small molecule associations (Chen et al, 2018b), genome-wide features (Xu et al, 2018) and functional impact of variants (Milanese et al, 2019;Rojano et al, 2019;Xu et al, 2019). Of course, some machine learning models were developed for predicting potential associations between miRNAs and diseases (Chen et al, 2018a,c,g;Wang et al, 2019). For example, the model of Restricted Boltzmann Machine for Multiple types of MiRNA-Disease Association prediction (RBMMMDA) was proposed by Chen et al (2015).…”

Section: Introductionmentioning

confidence: 99%

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

Peng

Zhou

Chen

et al. 2020

Front. Bioeng. Biotechnol.

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As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model's average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

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“…The model had the disadvantage that it was often difficult to obtain the negative samples that were required during the training process. Wang et al [46] constructed the Negative Samples Extraction method (NSEMDA) to predict miRNA-disease association pairs. The model used the Spy and Rocchio techniques to exclude negative samples and calculated similarity for suspected negative samples.…”

Section: Introductionmentioning

confidence: 99%

Identifying Potential miRNA-Disease Associations Based on an Improved Manifold Learning Framework

Tang

et al. 2020

IEEE Access

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Adequate evidence has shown that miRNA-disease interactions are strongly involved in the pathological processes of complex human diseases. However, it is commonly time-consuming and laborintensive to utilize laboratory biological experiments to reveal unknown miRNA-disease pairs. Since the previously proposed calculation model has more or fewer deficiencies, we developed the semi-supervised method called Hessian Regularized Non-negative Matrix Factorization Method for miRNA-disease Association prediction (HRNMFMDA). This model introduced Hessian regularization into the NMF framework to preserve the local manifold information and increased an l 2,1-norm penalty term to ensure the feature selection of the coding matrix and an approximate orthogonal constraint to obtain discriminative information. In the model performance evaluation, HRNMFMDA outperformed eight existing models, achieving AUC values of 0.9074, 0.8618, and 0.9044+/−0.0080 in the global leave-one-out cross-validation (LOOCV), local LOOCV and 5-fold cross-validation, respectively. In addition, we applied HRNMFMDA to several highincidence human carcinomas via three kinds of case studies. Almost all predicted miRNAs were confirmed by external databases derived from experimental literature. Therefore, the conclusion can be drawn that HRNMFMDA is reliable for revealing uncovered miRNA-disease pairs.

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An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy

Cited by 35 publications

References 51 publications

MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

Identifying Potential miRNA-Disease Associations Based on an Improved Manifold Learning Framework

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