Predicting miRNA-disease associations based on lncRNA–miRNA interactions and graph convolution networks

Wang, Wengang; Chen, Hailin

doi:10.1093/bib/bbac495

Cited by 18 publications

(10 citation statements)

References 36 publications

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“…This model achieved a high AUC of 0.952 when combining H&E image with DNA methylation data. Moreover, a model built on graph convolution networks with a multichannel attention mechanism predicted miRNA–disease associations based on lncRNA–miRNA interactions, achieving high average AUC values in different cross-validation setups [ 99 ].…”

Section: Other Deep Learning Research Domains and Utilization Of Lncr...mentioning

confidence: 99%

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

Kim

Lee

2023

IJMS

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This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.

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Section: Other Deep Learning Research Domains and Utilization Of Lncr...mentioning

confidence: 99%

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

Kim

Lee

2023

IJMS

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“…Wang et al . [ 25 ] designed the MAGCN method based on known lncRNA–miRNA interactions and graph convolution networks without using any similarity measurements. This method predicts miRNA-disease associations by using GCN with multichannel attention mechanism and convolutional neural network combiner.…”

Section: Introductionmentioning

confidence: 99%

“…Dong et al [24] proposed a multi-task graph convolutional learning framework named MuCoMiD, which integrates knowledge from five heterogeneous biological information sources and allows automatic feature extraction in an end-to-end manner to predict the associations between miRNAs and diseases. Wang et al [25] designed the MAGCN method based on known lncRNA-miRNA interactions and graph convolution networks without using any similarity measurements. This method predicts miRNA-disease associations by using GCN with multichannel attention mechanism and convolutional neural network combiner.…”

Section: Introductionmentioning

confidence: 99%

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations

Ouyang,

Liang,

Wang

et al. 2024

PLoS Comput Biol

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Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

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“…GCNDTI [ 32 ] constructed a drug-protein pair network and treated node pairs as independent nodes, which transformed the link prediction problem into a node pair classification problem. MAGCN [ 33 ] introduced lncRNA-miRNA interactions and miRNA–disease associations to represent miRNAs and diseases, then did MDA predictions via graph convolution networks with the multi-channel attention mechanism and convolutional neural network combiner. MKGAT [ 34 ] applied multi-layer GAT to update miRNA or disease features and then fused them through the attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

MGCNSS: miRNA–disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy

Tian,

Han,

et al. 2024

Briefings in Bioinformatics

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Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master

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Predicting miRNA-disease associations based on lncRNA–miRNA interactions and graph convolution networks

Cited by 18 publications

References 36 publications

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations

MGCNSS: miRNA–disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy

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