Deep-belief network for predicting potential miRNA-disease associations

Chen, Xing; Li, Tianhao; Zhao, Yan; Wang, Chun-Chun; Zhu, Chi-Chi

doi:10.1093/bib/bbaa186

Cited by 130 publications

(64 citation statements)

References 50 publications

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“…Finally, more useful information sources including the gene-disease associations can be integrated into the feature vectors of lncRNA-disease pairs to further improve the prediction performance of FVTLDA. In the future, we can also study the association prediction in various fields of computational biology, such as miRNA-disease association prediction [35][36][37], drugtarget interaction prediction [38,39], and then bring valuable insights to the development of lncRNA-disease association prediction.…”

Section: Discussionmentioning

confidence: 99%

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

Xiao

Feng

et al. 2020

BMC Bioinformatics

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Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

show abstract

Section: Discussionmentioning

confidence: 99%

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

Xiao

Feng

et al. 2020

BMC Bioinformatics

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“…In order to verify the validity of the proposed feature representation approach, we compare it with other three methods. The first one is DBNMDA (Chen et al, 2020), which directly extracts the features of all miRNA-disease pairs to pretrain the Restricted Boltzmann Machine (RBM). The second one is DBMDA (Zheng et al, 2020), which utilizes the autoencoder to resize the miRNA (disease) similarity features and then fuses the features during the feature set construction stage.…”

Section: Analysis Of High-order Feature Extraction and Feature Interactionmentioning

confidence: 99%

PMDFI: Predicting miRNA–Disease Associations Based on High-Order Feature Interaction

Ming-yan

Liu

et al. 2021

Front. Genet.

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MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

show abstract

“…further improve the prediction performance of FVTLDA. In the future, we can also study the association prediction in various fields of computational biology, such as miRNA-disease association prediction [35][36][37], drug-target interaction prediction [38][39], and then bring valuable insights to the development of lncRNA-disease association prediction.…”

Section: Case Studymentioning

confidence: 99%

A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

Xiao

Feng

et al. 2020

Preprint

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Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

show abstract

Deep-belief network for predicting potential miRNA-disease associations

Cited by 130 publications

References 50 publications

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

PMDFI: Predicting miRNA–Disease Associations Based on High-Order Feature Interaction

A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

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