Benchmark of computational methods for predicting microRNA-disease associations

Huang, Zhou; Liu, Leibo; Gao, Yuanxu; Shi, Jiangcheng; Cui, Qinghua; Li, Jianwei; Zhou, Yuan

doi:10.1186/s13059-019-1811-3

Cited by 39 publications

(20 citation statements)

References 58 publications

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“…We train our model and other methods on these datasets for the same setting. As for testing, the data is chosen from benchmark2019 dataset [ 29 ] as the independent test set to make a relatively fair comparison.…”

Section: Gcsenet Prediction Modelmentioning

confidence: 99%

GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

et al. 2021

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Recently, an increasing number of studies have demonstrated that miRNAs are involved in human diseases, indicating that miRNAs might be a potential pathogenic factor for various diseases. Therefore, figuring out the relationship between miRNAs and diseases plays a critical role in not only the development of new drugs, but also the formulation of individualized diagnosis and treatment. As the prediction of miRNA-disease association via biological experiments is expensive and time-consuming, computational methods have a positive effect on revealing the association. In this study, a novel prediction model integrating GCN, CNN and Squeeze-and-Excitation Networks (GCSENet) was constructed for the identification of miRNA-disease association. The model first captured features by GCN based on a heterogeneous graph including diseases, genes and miRNAs. Then, considering the different effects of genes on each type of miRNA and disease, as well as the different effects of the miRNA-gene and disease-gene relationships on miRNA-disease association, a feature weight was set and a combination of miRNA-gene and disease-gene associations was added as feature input for the convolution operation in CNN. Furthermore, the squeeze and excitation blocks of SENet were applied to determine the importance of each feature channel and enhance useful features by means of the attention mechanism, thus achieving a satisfactory prediction of miRNA-disease association. The proposed method was compared against other state-of-the-art methods. It achieved an AUROC score of 95.02% and an AUPR score of 95.55% in a 10-fold cross-validation, which led to the finding that the proposed method is superior to these popular methods on most of the performance evaluation indexes.

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Section: Gcsenet Prediction Modelmentioning

confidence: 99%

GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

et al. 2021

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“…Unfortunately, current research mostly focuses on identifying the associations between diseases and genes (ncRNAs) [7][8][9][10][11]. Wu et al [12] proposed a method called REGENT for integrating multiple gene networks with GWAS data to prioritize complex disease-associated genes.…”

Section: Introductionmentioning

confidence: 99%

DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

Yang

Yan

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the associations between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses geneisoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two subnetworks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC (area under the precision-recall curve) of 0.9141, macro Fmeasure of 0.9155, G-mean of 0.9278 and balanced accuracy of 0.9303 across 732 diseases, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborates the superiority of DeepIDA. The code of DeepIDA is available at http://mlda.swu.edu.cn/codes.php?name=DeepIDA.

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“…Previous researches observe that similar miRNAs tend to associate with the same diseases and similar diseases are highly likely related to the same miRNAs. Hence, many computational methods construct disease similarity network and miRNA similarity network and infer miRNA-disease associations based on the associations between or within the disease or miRNAs ( Peng et al, 2016 , 2017 ; Zou et al, 2016 ; Huang et al, 2019 ). Xuan et al (2013) construct a miRNA similarity network according to the degree of two miRNAs sharing similar disease and consider the k most similar neighbors of each miRNA to infer miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding

Peng

Dai

et al. 2021

Front. Cell Dev. Biol.

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MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods.

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Benchmark of computational methods for predicting microRNA-disease associations

Cited by 39 publications

References 58 publications

GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding

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