Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection

Toprak, Ahmet; Eryilmaz, Esma

doi:10.1142/s0219720020500419

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…In this study, we used the Kernelized Bayesian matrix factorization method to predict possible relationships between miRNAs and diseases. In our previous study (Toprak & Eryilmaz, 2020), high quality results were obtained by using similar approaches with a different method. We evaluated the predictive performance of our model with 5-fold cross validation technique and several case studies.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Toprak¹,

DOĞAN²

2021

European Journal of Science and Technology

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MicroRNA (miRNA) molecules, which are effective on the initiation and progression of many different diseases, are a type of noncoding RNA with a length of about 22 nucleotides. Scientists have reported the importance of miRNAs in the prevention, diagnosis, and treatment of complex human diseases. Therefore, in the last decade, researchers have been working hard to find potential miRNAdisease associations. Many computational techniques have been developed because of the experimental techniques are time-consuming and expensive used to find new relationships between miRNAs and diseases. In this study, we suggested Kernelized Bayesian matrix factorization (KBMF) technique to predict new miRNA-disease relationships. We applied 5-fold cross validation technique and obtained an average value AUC of 0.9450. Also, we applied case studies based on breast, lung, and colon neoplasms to prove the performance of KBMF technique. The results showed that KBMF can be used as a reliable computational model to reveal possible miRNA-disease relationships.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Toprak¹,

DOĞAN²

2021

European Journal of Science and Technology

Self Cite

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“…The model constructs the topological similarity between miRNAs and diseases based on diverse similarity measures, makes full use of the network topology information, and obtains the stable probability of each miRNA-disease pair to prioritize miRNA candidates. Toprak A [19] proposes weighted nearest known neighbors and network consistent projection techniques. The method predicts miRNA-disease associations by using known similarity networks.…”

Section: Introductionmentioning

confidence: 99%

HGCN_MDA: Hierarchical Heterogeneous GraphConvolutional Neural Network for PredictingmiRNA-Disease Associations

Li,

Yang,

Sun

et al. 2024

Preprint

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miRNAs are non-coding, single-stranded RNA molecules approximately 22 nucleotides in length. They play pivotal roles in numerous biological regulatory processes and have close associations with a variety of diseases, including cancer, cardiovascular diseases, and neurodegenerative disorders. miRNAs have become new candidate molecular targets for disease diagnosis and treatment. Therefore, the swift and effective identification of associations between miRNAs and diseases holds significant clinical significance. Presently, the available methods for predicting miRNA-disease associations remain quite limited. Moreover, there is a scarcity of approaches that specifically address the varying importance of different nodes in miRNA and disease interactions, and then model these variations. Consequently, there is an urgent and challenging need to develop computational models for forecasting potential miRNA-disease associations. In this paper, we propose a hierarchical heterogeneous graph convolutional network named HGCN_MDA for the prediction of miRNA-disease associations. Our approach encompasses several key steps: First, we gather data on the associations between miRNAs and diseases, miRNAs and miRNAs, and diseases and diseases. Subsequently, we compute the importance scores of nodes in the graph, which serve as indicators for stratifying the hierarchical network. We then construct a multi-layer network to extract features based on their respective importance. Our approach combines global and local features, which are integrated by the attention mechanism. Finally, a fully connected layer is used to build a prediction model. Comprehensive experimental comparisons demonstrate that our proposed HGCN_MDA has better ability to predict positive samples and comprehensive performance.

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“…LRWRHLDA, Laplace normalization of similarities and interactions between diseases, genes and noncoding RNAs, can be used to make a final prediction after several rounds of iterative training, and a similar method is MHRWR . Birandom walks are also available in MSF-UBRW, where the interaction between lncRNAs and diseases was reconstructed using WKNKN based on unbalanced birandom walks and used as a transfer matrix for the respective networks. Similar birandom walk methods are used in NCP-BiRW and lung cancer prediction .…”

Section: Introductionmentioning

confidence: 99%

Predicting lncRNA–Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration

Yao,

Zhang,

Zhan

et al. 2024

ACS Omega

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Identifying the associations between long noncoding RNAs (lncRNAs) and disease is critical for disease prevention, diagnosis and treatment. However, conducting wet experiments to discover these associations is time-consuming and costly. Therefore, computational modeling for predicting lncRNA-disease associations (LDAs) has become an important alternative. To enhance the accuracy of LDAs prediction and alleviate the issue of node feature oversmoothing when exploring the potential features of nodes using graph neural networks, we introduce DPFELDA, a dual-path feature extraction network that leverages the integration of information from multiple sources to predict LDA. Initially, we establish a dual-view structure of lncRNAs and disease and a heterogeneous network of lncRNA-disease-microRNA (miRNA) interactions. Subsequently, features are extracted using a dual-path feature extraction network. In particular, we employ a combination of a graph convolutional network, a convolutional block attention module, and a node aggregation layer to perform multilayer topology feature extraction for the dual-view structure of lncRNAs and diseases. Additionally, we utilize a Transformer model to construct the node topology feature residual network for obtaining node-specific features in heterogeneous networks. Finally, XGBoost is employed for LDA prediction. The experimental results demonstrate that DPFELDA outperforms the benchmark model on various benchmark data sets. In the course of model exploration, it becomes evident that DPFELDA successfully alleviates the issue of node feature oversmoothing induced by graph-based learning. Ablation experiments confirm the effectiveness of the innovative module, and a case study substantiates the accuracy of DPFELDA model in predicting novel LDAs for characteristic diseases.

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Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection

Cited by 9 publications

References 39 publications

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

HGCN_MDA: Hierarchical Heterogeneous GraphConvolutional Neural Network for PredictingmiRNA-Disease Associations

Predicting lncRNA–Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration

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