Predicting miRNA–Disease Associations Through Deep Autoencoder With Multiple Kernel Learning

Zhou, Feng; Yin, Meng-Meng; Jiao, Cui-Na; Jing-xiu, Zhao; Zheng, Chun-Hou; Liu, Jin-Xing

doi:10.1109/tnnls.2021.3129772

Cited by 20 publications

(12 citation statements)

References 41 publications

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“…Li et al (2021a) used a similarity network fusion algorithm to integrate the similarity of multiple miRNAs and diseases, and graph Laplacian regularization was constrained to matrix factorization to predict miRNA-disease associations. Zhou et al (2021) utilized multiple kernel learning to construct similarity networks between miRNA and disease, and a regression model was used to learn feature representation based on these networks. Then, these feature representations are input into a deep autoencoder to predict miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition

Ouyang

Miao

Wang

et al. 2022

Front. Bioeng. Biotechnol.

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Many studies have indicated miRNAs lead to the occurrence and development of diseases through a variety of underlying mechanisms. Meanwhile, computational models can save time, minimize cost, and discover potential associations on a large scale. However, most existing computational models based on a matrix or tensor decomposition cannot recover positive samples well. Moreover, the high noise of biological similarity networks and how to preserve these similarity relationships in low-dimensional space are also challenges. To this end, we propose a novel computational framework, called WeightTDAIGN, to identify potential multiple types of miRNA–disease associations. WeightTDAIGN can recover positive samples well and improve prediction performance by weighting positive samples. WeightTDAIGN integrates more auxiliary information related to miRNAs and diseases into the tensor decomposition framework, focuses on learning low-rank tensor space, and constrains projection matrices by using the L2,1 norm to reduce the impact of redundant information on the model. In addition, WeightTDAIGN can preserve the local structure information in the biological similarity network by introducing graph Laplacian regularization. Our experimental results show that the sparser datasets, the more satisfactory performance of WeightTDAIGN can be obtained. Also, the results of case studies further illustrate that WeightTDAIGN can accurately predict the associations of miRNA–disease-type.

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Section: Introductionmentioning

confidence: 99%

Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition

Ouyang

Miao

Wang

et al. 2022

Front. Bioeng. Biotechnol.

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show abstract

“… Yin et al (2020) proposed a computational method, NCPLP, which is based on the network consistency projection and label propagation to predict disease-associated microbes. Zhou et al (2021) proposed a novel method that learns features through multiple kernel learning and deep autoencoder, finally predicting new microRNA -disease associations by reconstruction error. Pan et al (2022) combined protein attribute and behavior vectors and used a deep neural network (DNN) to fuse the protein feature vector to predict the protein–protein interactions.…”

Section: Introductionmentioning

confidence: 99%

KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning

Wang

et al. 2022

Front. Genet.

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Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing the circRNA–miRNA interaction provides a new perspective for the detection and treatment of human complex diseases. Compared with the traditional biological experimental methods used to predict the association of molecules, which are limited to the small-scale and are time-consuming and laborious, computing models can provide a basis for biological experiments at low cost. Considering that the proposed calculation model is limited, it is necessary to develop an effective computational method to predict the circRNA–miRNA interaction. This study thus proposed a novel computing method, named KGDCMI, to predict the interactions between circRNA and miRNA based on multi-source information extraction and fusion. The KGDCMI obtains RNA attribute information from sequence and similarity, capturing the behavior information in RNA association through a graph-embedding algorithm. Then, the obtained feature vector is extracted further by principal component analysis and sent to the deep neural network for information fusion and prediction. At last, KGDCMI obtains the prediction accuracy (area under the curve [AUC] = 89.30% and area under the precision–recall curve [AUPR] = 87.67%). Meanwhile, with the same dataset, KGDCMI is 2.37% and 3.08%, respectively, higher than the only existing model, and we conducted three groups of comparative experiments, obtaining the best classification strategy, feature extraction parameters, and dimensions. In addition, in the performed case study, 7 of the top 10 interaction pairs were confirmed in PubMed. These results suggest that KGDCMI is a feasible and useful method to predict the circRNA–miRNA interaction and can act as a reliable candidate for related RNA biological experiments.

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“…Recently, machine learning methods have been applied to miRNA-disease potential association prediction [ Jiang et al (2013) ; Chen and Yan (2014) ; Chen et al (2018a) ; Zheng et al (2019) ; Zeng et al (2019) ; Liang et al (2019) ; Li et al (2020) ; Zhou et al (2021) ]. For example, Jiang et al [ Jiang et al (2013) ] used support vector machine (SVM) to predict miRNA-disease interaction.…”

Section: Introductionmentioning

confidence: 99%

Predict potential miRNA-disease associations based on bounded nuclear norm regularization

Rao

Xie²,

Wang³

2022

Front. Genet.

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Increasing evidences show that the abnormal microRNA (miRNA) expression is related to a variety of complex human diseases. However, the current biological experiments to determine miRNA-disease associations are time consuming and expensive. Therefore, computational models to predict potential miRNA-disease associations are in urgent need. Though many miRNA-disease association prediction methods have been proposed, there is still a room to improve the prediction accuracy. In this paper, we propose a matrix completion model with bounded nuclear norm regularization to predict potential miRNA-disease associations, which is called BNNRMDA. BNNRMDA at first constructs a heterogeneous miRNA-disease network integrating the information of miRNA self-similarity, disease self-similarity, and the known miRNA-disease associations, which is represented by an adjacent matrix. Then, it models the miRNA-disease prediction as a relaxed matrix completion with error tolerance, value boundary and nuclear norm minimization. Finally it implements the alternating direction method to solve the matrix completion problem. BNNRMDA makes full use of available information of miRNAs and diseases, and can deals with the data containing noise. Compared with four state-of-the-art methods, the experimental results show BNNRMDA achieved the best performance in five-fold cross-validation and leave-one-out cross-validation. The case studies on two complex human diseases showed that 47 of the top 50 prediction results of BNNRMDA have been verified in the latest HMDD database.

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Predicting miRNA–Disease Associations Through Deep Autoencoder With Multiple Kernel Learning

Cited by 20 publications

References 41 publications

Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition

Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition

KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning

Predict potential miRNA-disease associations based on bounded nuclear norm regularization

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