Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

Chen, Jin; Shi, Zhuangwei; Lin, Ken; Zhang, Han

doi:10.3390/biom12010064

Cited by 32 publications

(9 citation statements)

References 64 publications

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“…To demonstrate the performance of MVIFMDA in identifying potential disease-related miRNAs, we compared it with six state-of-the-art approaches that were developed for MDA prediction, including MDHGI ( Chen et al, 2018b ), ABMDA ( Zhao et al, 2019 ), NIMGSA ( Jin et al, 2022 ), NIMCGCN ( Li et al, 2020 ), DANE-MDA ( Ji et al, 2021 ), MMGCN ( Tang et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Predicting miRNA-disease associations based on multi-view information fusion

et al. 2022

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MicroRNAs (miRNAs) play an important role in various biological processes and their abnormal expression could lead to the occurrence of diseases. Exploring the potential relationships between miRNAs and diseases can contribute to the diagnosis and treatment of complex diseases. The increasing databases storing miRNA and disease information provide opportunities to develop computational methods for discovering unobserved disease-related miRNAs, but there are still some challenges in how to effectively learn and fuse information from multi-source data. In this study, we propose a multi-view information fusion based method for miRNA-disease association (MDA)prediction, named MVIFMDA. Firstly, multiple heterogeneous networks are constructed by combining the known MDAs and different similarities of miRNAs and diseases based on multi-source information. Secondly, the topology features of miRNAs and diseases are obtained by using the graph convolutional network to each heterogeneous network view, respectively. Moreover, we design the attention strategy at the topology representation level to adaptively fuse representations including different structural information. Meanwhile, we learn the attribute representations of miRNAs and diseases from their similarity attribute views with convolutional neural networks, respectively. Finally, the complicated associations between miRNAs and diseases are reconstructed by applying a bilinear decoder to the combined features, which combine topology and attribute representations. Experimental results on the public dataset demonstrate that our proposed model consistently outperforms baseline methods. The case studies further show the ability of the MVIFMDA model for inferring underlying associations between miRNAs and diseases.

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Section: Resultsmentioning

confidence: 99%

Predicting miRNA-disease associations based on multi-view information fusion

et al. 2022

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“…Therefore, dysregulations or abnormalities of miRNAs, including epigenetic silencing and expression de-regulation, are important for the development of many diseases, including lung cancer, breast cancer, and cardiovascular diseases ( Dai et al, 2022 ). For example, previous research has shown that abnormal expression of hsa-mir-21 can affect the proliferation of several kinds of tumor cells, such as glioblastoma, breast and pancreatic neoplasms ( Jin et al, 2022 ). Similarly, ( Zhong et al, 2022 ), showed that the downregulation of miR-143/miR-145 and miR-15a/miR-16-1 could result in colon cancer and lung cancer, respectively ( Zhong et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

et al. 2023

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During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

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“…Based on MCMDA, Chen et al designed a modified model IMCMDA [ 63 ] and NCMCMDA [ 64 ]. In addition, a series of improved models have emerged, such as the improved inductive matrix complementary model (IIMCMP) [ 65 ], IMDN model with the addition of biased network regularities [ 66 ], neural induction matrix complementation method model (NIMGSA) combined with graph auto-encoder and self-attention mechanism [ 67 ], matrix complementation algorithm and label passing algorithm model (MCLPMDA) [ 68 ], miRTMC model combining the matrix complementation algorithm with kernel parametric regularized linear least squares under non-negative constraints [ 69 ], and DLRMC combining matrix complementation algorithm with double Laplace regularization [ 70 ]. These improvements enabled the matrix decomposition model to be scalable.…”

Section: Introductionmentioning

confidence: 99%

KATZNCP: a miRNA–disease association prediction model integrating KATZ algorithm and network consistency projection

Chen

Deng

et al. 2023

BMC Bioinformatics

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Background Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA–disease associations predicted by computational methods are the best complement to biological experiments. Results In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA–disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA–disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA–disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. Conclusion A new computational model KATZNCP was proposed for predicting potential miRNA–drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA–disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

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Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

Cited by 32 publications

References 64 publications

Predicting miRNA-disease associations based on multi-view information fusion

Predicting miRNA-disease associations based on multi-view information fusion

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

KATZNCP: a miRNA–disease association prediction model integrating KATZ algorithm and network consistency projection

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