WBNPMD: weighted bipartite network projection for microRNA-disease association prediction

et al. 2020

BMC Bioinformatics

Background MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. Results The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). Conclusions The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

Section: Comparison With Other Methodsmentioning

confidence: 74%

MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

et al. 2020

BMC Bioinformatics

“…MCCMF finally obtains an AUC value of 0.9563 in the five-fold cross validation. MCCMF is compared with four advanced methods such as WBNPMD [43], RLSMDA [44], GRNMF [21] and CMF [45], which proves the superior performance of our method. The ROC curves are drawn in Fig.1, and the comparison results are listed in Table 1.…”

Section: Comparison With Other Methodsmentioning

confidence: 74%

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

et al. 2020

Preprint

Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

“…MCCMF finally obtains an AUC value of 0.9563 in the five-fold cross validation. MCCMF is compared with four advanced methods such as WBNPMD [51], RLSMDA [52], GRNMF [21] and CMF [49], which proves the superior performance of our method. The ROC curves are drawn in Fig.…”

Section: Mccmf For Mirna-disease Association Predictionmentioning

confidence: 74%

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

et al. 2020

Preprint

Background: MicroRNAs (MiRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005). Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, f-measure and other evaluation indexes are also added. The final experimental results demonstrate that MCCMF outperforms other methods in prediction miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.