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

Wu, Tian-Ru; Yin, Meng-Meng; Jiao, Cui-Na; Gao, Ying-Lian; Kong, Xiang-Zhen; Liu, Jin-Xing

doi:10.1186/s12859-020-03799-6

Cited by 13 publications

(7 citation statements)

References 55 publications

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“…There are some factors which contribute to the desirable performance of our proposed method as follows. Firstly, the known miRNA–disease associations which includes 5430 experimentally verified associations between 383 diseases and 495 miRNAs were gathered from the HMDD V2.0 database are reliable and they were used in many recent researches 4 , 14 , 27 . Secondly, both AUC and AUPR values of the proposed method were increased by using integrated similarities although it did not reduce the effect of sparsity data problem.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…One of these limitations is the problem of sparsity and incompletion of data that affected prediction accuracies. In recent years, a weighted K-nearest known neighbors (WKNKN) algorithm was usually used as a pre-processing step to eliminate unknown values in miRNA–disease association set as in the studies of Ezzat et al 25 , Gao et al 26 , Wu et al 27 , and Li et al 28 . It relied on the fact the number of known miRNA‐disease associations are very limited in comparison with the number of non-interacting miRNA–disease pairs which are unknown cases that could potentially be accurate associations in the training datasets.…”

Section: Introductionmentioning

confidence: 99%

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Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Nguyen

Than

et al. 2021

Sci Rep

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Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA–disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA–disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Nguyen

Than

et al. 2021

Sci Rep

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“…miRNAsMiRNAs are involved in many important processes, such as signal transduction, tissue development, apoptosis [17], proliferation [18], and others [19]. Thus, modulated miRNA expression is associated with various human diseases [20,21]. This has been reported in several studies.…”

Section: Introductionmentioning

confidence: 98%

“…In addition, miR-340 has been proposed as a biomarker for cancer prognosis. The known associations between miRNAs and diseases are documented in various databases, including HMDD [24] and mir2Disease [20,25]. The effects of miRNAs on different disease activities shed light on new treatment perspectives if they can be controlled by small molecules (SMs) [26].…”

Section: Introductionmentioning

confidence: 99%

Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders

2021

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MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.

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“…Since their development, machine learning methods have been widely used in biomedical research [ 13 , 14 , 15 ]. Wu et al [ 16 ] built and optimized an miRNA–disease adjacency matrix and used the collaborative matrix decomposition method to obtain a representation matrix of miRNA and disease. Chen et al [ 17 ] combined miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity calculations into the comprehensive similarity of miRNA and disease.…”

Section: Introductionmentioning

confidence: 99%

MEAHNE: miRNA–Disease Association Prediction Based on Semantic Information in a Heterogeneous Network

Huang

Cen

Zhang

et al. 2022

Life

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Correct prediction of potential miRNA–disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision–recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases.

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

Cited by 13 publications

References 55 publications

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders

MEAHNE: miRNA–Disease Association Prediction Based on Semantic Information in a Heterogeneous Network

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