LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations

Yin, Meng-Meng; Cui, Zhen; Gao, Mingming; Liu, Jin-Xing; Gao, Ying-Lian

doi:10.1109/tcbb.2019.2937774

Cited by 21 publications

(11 citation statements)

References 35 publications

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“…Besides, limitation of knowledge about diseases, lncRNA, and miRNAs constrain the prediction performance of RFLDA. Finally, there are many excellent association prediction computational models in various fields of computational biology, such as miRNA/lncRNA-disease association prediction [57][58][59][60][61][62], drug-target interaction prediction [63], and synergistic drug combination prediction [64]. These association prediction models would provide valuable insights into the development of new lncRNA-disease association prediction.…”

Section: Discussionmentioning

confidence: 99%

A random forest based computational model for predicting novel lncRNA-disease associations

Yao

Zhan

et al. 2020

BMC Bioinformatics

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Background: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results: To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions: Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

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Section: Discussionmentioning

confidence: 99%

A random forest based computational model for predicting novel lncRNA-disease associations

Yao

Zhan

et al. 2020

BMC Bioinformatics

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“…One of the most obvious disadvantages of NPCMF is that it introduces too much NP information, which may reduce the prediction accuracy while adding extra noise. In order to protect the known correlation, Logistic Weighted Profile-based Collaborative Matrix Factorization (LWPCMF) method was proposed by Yin et al, which effectively predicts miRNA-disease associations [27]. The prediction effect of this method is…”

Section: Gao Et Al Introduced a Methods Of Nearest Profile-based Collmentioning

confidence: 99%

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

Yin

Jiao

et al. 2020

Preprint

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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.

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Section: Gao Et Al Introduced a Methods Of Nearest Profile-based Collmentioning

confidence: 99%

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

Yin

Jiao

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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, 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.

show abstract

LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations

Cited by 21 publications

References 35 publications

A random forest based computational model for predicting novel lncRNA-disease associations

A random forest based computational model for predicting novel lncRNA-disease associations

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

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

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