MHDMF: Prediction of miRNA–disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network

Ai, Ning; Liang, Yong; Yuan, Hao-Laing; Ouyang, Dong; Li, Xiaoying; Xie, Shengli; Ji, Yongming

doi:10.1016/j.compbiomed.2022.106069

Cited by 11 publications

(10 citation statements)

References 60 publications

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“…10 Ai et al utilized MHDMF, a computational method that integrates multiple sources of information and utilizes Deep Matrix Factorization (DMF), to predict potential associations while making use of high false-negative associations. 11 Zheng et al offer a unified framework called NMFMC that integrates non-negative matrix factorization, matrix completion, and graph regularization to overcome the issue of sparsity in existing known associations. 12 Chen et al enhanced the original features of miRNA and disease by integrating the features obtained from matrix factorization with other similarity features, and they constructed a heterogeneous network using these enhanced original features.…”

Section: Introductionmentioning

confidence: 99%

“…By doing so, the proposed method can identify potential miRNA‐disease associations with high accuracy and reliability, which can be beneficial for further experimental validation 10 . Ai et al utilized MHDMF, a computational method that integrates multiple sources of information and utilizes Deep Matrix Factorization (DMF), to predict potential associations while making use of high false‐negative associations 11 . Zheng et al offer a unified framework called NMFMC that integrates non‐negative matrix factorization, matrix completion, and graph regularization to overcome the issue of sparsity in existing known associations 12 .…”

Section: Introductionmentioning

confidence: 99%

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DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

Jia,

Wang,

Xing

et al. 2024

Numer Methods Biomed Eng

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MiRNA (microRNA)‐disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time‐consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA‐disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention‐based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA‐disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high‐precision feature mining and association scoring prediction. We conducted a five‐fold cross‐validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1‐score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

Jia,

Wang,

Xing

et al. 2024

Numer Methods Biomed Eng

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“…Nonetheless, conventional biological experiments are not only time-consuming and costly but also inefficient and susceptible to external environmental factors [ 11 , 12 ]. With technological advancements, the field of bioinformatics has amassed a wealth of multi-source data, providing an opportunity for the development of efficient and cost-effective computational methods [ 13–16 ].…”

Section: Introductionmentioning

confidence: 99%

SGCLDGA: unveiling drug–gene associations through simple graph contrastive learning

Fan,

Zhang,

et al. 2024

Briefings in Bioinformatics

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Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug–gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models’ state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug–gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA’s superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA’s potential in discovering new drug–gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

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“…Unfortunately, there is a phenomenon where the existing open ncRNA-disease databases use 1 and 0 to indicate whether has relationship between them, with very few “1” values pointing a known association and very numerous “0” values pointing an unknown association rather than no association. This phenomenon we called false-negative, and there are many false-negative associations in ncRNA-disease databases, which will impact the performance and interpretability of computational methods [ 17 , 28 ]. Secondly, abundant previous works enhance performance of methods by fusing the similarity networks of ncRNAs and diseases by a simple average or linear weighting strategy.…”

Section: Introductionmentioning

confidence: 99%

“…It is an end-to-end computational framework, for integrating divers multi-source information on different HNs. Different from our previous work MHDMF [ 28 ], GDCL-NcDA introduces a deep graph learning (deep graph convolutional network-GCNII [ 30 ]), employs multiple attention mechanisms, including graph attention network (GAT) and multi-channel attention to enhance the characteristics of within and between similarity networks. GDCL-NcDA also uses DMF to identify potential associations while further adding contrastive learning (CL), which makes the GDCL-NcDA framework have better generalization and robustness.…”

Section: Introductionmentioning

confidence: 99%

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization

Ai¹,

Liang²,

Yuan³

et al. 2023

BMC Genomics

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Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good complement to wet experiment methods, computational prediction methods can greatly save experimental costs. However, high false-negative data and insufficient use of multi-source information can affect the performance of computational prediction methods. Furthermore, many computational methods do not have good robustness and generalization on different datasets. In this work, we propose an effective end-to-end computing framework, called GDCL-NcDA, of deep graph learning and deep matrix factorization (DMF) with contrastive learning, which identifies the latent ncRNA-disease association on diverse multi-source heterogeneous networks (MHNs). The diverse MHNs include different similarity networks and proven associations among ncRNAs (miRNAs, circRNAs, and lncRNAs), genes, and diseases. Firstly, GDCL-NcDA employs deep graph convolutional network and multiple attention mechanisms to adaptively integrate multi-source of MHNs and reconstruct the ncRNA-disease association graph. Then, GDCL-NcDA utilizes DMF to predict the latent disease-associated ncRNAs based on the reconstructed graphs to reduce the impact of the false-negatives from the original associations. Finally, GDCL-NcDA uses contrastive learning (CL) to generate a contrastive loss on the reconstructed graphs and the predicted graphs to improve the generalization and robustness of our GDCL-NcDA framework. The experimental results show that GDCL-NcDA outperforms highly related computational methods. Moreover, case studies demonstrate the effectiveness of GDCL-NcDA in identifying the associations among diversiform ncRNAs and diseases.

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MHDMF: Prediction of miRNA–disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network

Cited by 11 publications

References 60 publications

DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

SGCLDGA: unveiling drug–gene associations through simple graph contrastive learning

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization

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