SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

Li, Jianwei; Li, Jianing; Kong, Mengfan; Wang, Duanyang; Fu, Kun; Shi, Jiangcheng

doi:10.1186/s12859-021-04457-1

Cited by 14 publications

(8 citation statements)

References 72 publications

(43 reference statements)

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“…In order to evaluate the robustness of GATMDA, we further test the performance of GATMDA on another dataset named HMDD v3.2 [ 43 ]. We obtain the dataset of HMDD v3.2 from Li’s model [ 44 ], which includes 4189 interactions between 437 miRNAs and 431 diseases, 8172 relationships between 861 lncRNAs and 437 miRNAs, and 4518 lncRNA-disease correlations. To obtain a systematic and convincing comparison, we compare GATMDA method with several baselines on HMDD v3.2, including LAGCN [ 39 ], NEMII [ 22 ] and GCAEMDA [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

Predicting miRNA-disease associations based on graph attention network with multi-source information

Fang

Zhang

et al. 2022

BMC Bioinformatics

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Background There is a growing body of evidence from biological experiments suggesting that microRNAs (miRNAs) play a significant regulatory role in both diverse cellular activities and pathological processes. Exploring miRNA-disease associations not only can decipher pathogenic mechanisms but also provide treatment solutions for diseases. As it is inefficient to identify undiscovered relationships between diseases and miRNAs using biotechnology, an explosion of computational methods have been advanced. However, the prediction accuracy of existing models is hampered by the sparsity of known association network and single-category feature, which is hard to model the complicated relationships between diseases and miRNAs. Results In this study, we advance a new computational framework (GATMDA) to discover unknown miRNA-disease associations based on graph attention network with multi-source information, which effectively fuses linear and non-linear features. In our method, the linear features of diseases and miRNAs are constructed by disease-lncRNA correlation profiles and miRNA-lncRNA correlation profiles, respectively. Then, the graph attention network is employed to extract the non-linear features of diseases and miRNAs by aggregating information of each neighbor with different weights. Finally, the random forest algorithm is applied to infer the disease-miRNA correlation pairs through fusing linear and non-linear features of diseases and miRNAs. As a result, GATMDA achieves impressive performance: an average AUC of 0.9566 with five-fold cross validation, which is superior to other previous models. In addition, case studies conducted on breast cancer, colon cancer and lymphoma indicate that 50, 50 and 48 out of the top fifty prioritized candidates are verified by biological experiments. Conclusions The extensive experimental results justify the accuracy and utility of GATMDA and we could anticipate that it may regard as a utility tool for identifying unobserved disease-miRNA relationships.

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Section: Resultsmentioning

confidence: 99%

Predicting miRNA-disease associations based on graph attention network with multi-source information

Fang

Zhang

et al. 2022

BMC Bioinformatics

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“…While research exploring the application of these methods to biological networks is still in its early stages, there is considerable interest. These applications can be loosely divided into three categories; (1) drug-related applications , such as drug-target interactions (DTIs) (4–9), drug-disease interactions (10–12), drug side-effects (13, 14), drug-drug interactions (15–17), polypharmacy antagonistic effects (18, 19) and synergistic reactions in drug combination therapy (20); (2) protein-related applications , such as protein-protein interactions (PPIs) (21–24) and protein/gene disease interactions (25–31); and (3) transcriptomics-related applications , such as lncRNAs-diseases associations (32–35) and miRNAdisease associations (36–43) and many other applications (44–50).…”

Section: Introductionmentioning

confidence: 99%

“…(2) protein-related applications, such as protein-protein interactions (PPIs) (21)(22)(23)(24) and protein/gene disease interactions (25)(26)(27)(28)(29)(30)(31); and (3) transcriptomics-related applications, such as lncRNAs-diseases associations (32)(33)(34)(35) and miRNAdisease associations (36)(37)(38)(39)(40)(41)(42)(43) and many other applications (44)(45)(46)(47)(48)(49)(50). Since network embedding methods were not originally developed for biological networks, their performance in obtaining different biological network features is yet to be established.…”

Section: Introductionmentioning

confidence: 99%

BioNE: Integration of network embeddings for supervised learning

Parvizi

Azuaje

Τheodoratou

et al. 2022

Preprint

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SummaryA network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology and bioinformatics. Several network embedding approaches have been proposed with different methods of generating vector representations. These network embedding approaches can be quite diverse in terms of data representation and implementation. Moreover, most were not originally developed for biological networks. Therefore comparing and assessing the performance of these diverse models in practice, in biological contexts, can be challenging. To facilitate such comparisons, we have developed the BioNE framework for integration of different embedding methods in prediction tasks. Using this framework one can easily assess, for instance, whether combined vector representations from multiple embedding methods offer complementary information with regards to the network features and thus better performance on prediction tasks. In this paper, we present the BioNE software suite for embedding integration, which applies network embedding methods following standardised network preparation steps, and integrates the vector representations achieved by these methods using three different techniques. BioNE enables selection of prediction models, oversampling methods, feature selection methods, cross-validation type and cross-validation parameters.Availability and implementationBioNE pipeline and detailed explanation of implementation is freely available on GitHub, at https://github.com/pooryaparvizi/BioNE

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“…Therefore, to consider the global topological information, several studies have tried to use random walk with restart to capture the global topological information ( Valdeolivas et al, 2019 ). Furthermore, other network representation methods, like node2vec, are used to predict disease ( Li et al, 2021 ). However, these methods are not designed for predicting the miRNA associated with Multiple Sclerosis.…”

Section: Introductionmentioning

confidence: 99%

Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods

Sun¹,

Ren²,

Zhang³

et al. 2022

Front. Genet.

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Identifying biomarkers of Multiple Sclerosis is important for the diagnosis and treatment of Multiple Sclerosis. The existing study has shown that miRNA is one of the most important biomarkers for diseases. However, few existing methods are designed for predicting Multiple Sclerosis-related miRNAs. To fill this gap, we proposed a novel computation framework for predicting Multiple Sclerosis-associated miRNAs. The proposed framework uses a network representation model to learn the feature representation of miRNA and uses a deep learning-based model to predict the miRNAs associated with Multiple Sclerosis. The evaluation result shows that the proposed model can predict the miRNAs associated with Multiple Sclerosis precisely. In addition, the proposed model can outperform several existing methods in a large margin.

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SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

Cited by 14 publications

References 72 publications

Predicting miRNA-disease associations based on graph attention network with multi-source information

Predicting miRNA-disease associations based on graph attention network with multi-source information

BioNE: Integration of network embeddings for supervised learning

Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods

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