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

Zhu, Xianyou; Wang, Xuzai; Zhao, Haochen; Pei, Tingrui; Kuang, Linai; Wang, Lei

doi:10.3389/fgene.2020.00384

Cited by 7 publications

(5 citation statements)

References 52 publications

(53 reference statements)

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“…We construct the features by integrating miRNA functional similarity, disease semantic similarity, and using Gaussian kernel functions, which is similar to several other methods [ 14 , 16 , 18 – 22 , 24 – 26 ].…”

Section: Methodsmentioning

confidence: 99%

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ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

2021

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Background A growing proportion of research has proved that microRNAs (miRNAs) can regulate the function of target genes and have close relations with various diseases. Developing computational methods to exploit more potential miRNA-disease associations can provide clues for further functional research. Results Inspired by the work of predecessors, we discover that the noise hiding in the data can affect the prediction performance and then propose an anti-noise algorithm (ANMDA) to predict potential miRNA-disease associations. Firstly, we calculate the similarity in miRNAs and diseases to construct features and obtain positive samples according to the Human MicroRNA Disease Database version 2.0 (HMDD v2.0). Then, we apply k-means on the undetected miRNA-disease associations and sample the negative examples equally from the k-cluster. Further, we construct several data subsets through sampling with replacement to feed on the light gradient boosting machine (LightGBM) method. Finally, the voting method is applied to predict potential miRNA-disease relationships. As a result, ANMDA can achieve an area under the receiver operating characteristic curve (AUROC) of 0.9373 ± 0.0005 in five-fold cross-validation, which is superior to several published methods. In addition, we analyze the predicted miRNA-disease associations with high probability and compare them with the data in HMDD v3.0 in the case study. The results show ANMDA is a novel and practical algorithm that can be used to infer potential miRNA-disease associations. Conclusion The results indicate the noise hiding in the data has an obvious impact on predicting potential miRNA-disease associations. We believe ANMDA can achieve better results from this task with more methods used in dealing with the data noise.

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Section: Methodsmentioning

confidence: 99%

“…Ha et al [ 17 ] utilized a matrix factorization method to predict miRNA-disease associations (PMAMCA). Zhu et al [ 18 ] used the biased heat conduction (BHCMDA) to pay more attention to unpopular nodes and improve the final results. Recently, ensemble learning methods have been designed to solve this problem and achieve great success.…”

Section: Introductionmentioning

confidence: 99%

ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

2021

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“…And they utilized k-means clustering on negative samples to perform random sampling, which could control the balance between positive samples and negative samples. The BHCMDA [24] model utilized biased heat conduction (BHC) algorithm to predict unknown connections between miRNAs and diseases though combining miRNA similarity matrix, disease similarity matrix and miRNA-disease association matrix. The probabilistic matrix factorization (PMF) algorithm was used in IMIPMF [25] model to infer potential miRNA-disease interactions.…”

Section: Introductionmentioning

confidence: 99%

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization

Gao

Wang

et al. 2021

PLoS Comput Biol

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miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

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“…The disadvantage of this model was that miRNAs might associate with more known diseases owning to the bias. The BHCMDA ( Zhu et al, 2020 ) model utilized biased heat conduction (BHC) algorithm to predict unknown connections between miRNAs and diseases through combining miRNA similarity matrix, disease similarity matrix, and miRNA–disease association matrix. The probabilistic matrix factorization (PMF) algorithm was used in IMIPMF ( Ha et al, 2020 ) model to infer potential miRNA–disease interactions.…”

Section: Introductionmentioning

confidence: 99%

SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction

Gao

Zheng

et al. 2021

Front. Cell Dev. Biol.

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MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA–disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA–disease.

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

Cited by 7 publications

References 52 publications

ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization

SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction

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