Predicting microRNA‐disease association based on microRNA structural and functional similarity network

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

show abstract

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Chu

Wang

Dai

et al. 2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

Prediction Models based on miRNA-disease Relationship: Diagnostic Relevance to Multiple Diseases Including COVID-19

Sharma

Sharma²,

Yadav

et al. 2023

CPB

View full text Add to dashboard Cite

Background: Small, non-coding microRNAs, usually of 20–25 nucleotides, are known to regulate the post-transcriptional gene expression, which has a significant role in human biological processes, including immune-biogenesis, homeostasis and infection control as differential expression of such miRNAs is responsible for fine-tuning the organismic development. Methods: A search of bibliographic databases was carried out with a focused question on microRNA-Disease Prediction. A deductive qualitative content analysis approach was further employed to assess the research's overall outcomes, review articles on prediction tools in miRNA-Diseases, and analyse the interventions. Results: Diagnosis and therapeutics of diseases and miRNA prediction methods hold importance in identifying the regulatory mechanisms. Collections of efficient miRNA prediction methods to identify miRNA-mRNA-disease regulatory relationships have been presented through this review, consolidating the potential of miRNAs as a diagnostic and prognostic biomarker of multiple diseases, including COVID-19. Conclusions: The role of miRNA in the aetiology and pathogenesis of wide-range of pathologies, including viral, bacterial to chronic diseases such as cancer, is quite feasible through the modern tools in bioinformatics which has been elaborated focusing upon miRNA-disease prediction methods and their application potential establishing miRNAs as a robust and reliable biomarker in clinico-medical studies.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting microRNA‐disease association based on microRNA structural and functional similarity network

Cited by 2 publications

References 39 publications

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

Prediction Models based on miRNA-disease Relationship: Diagnostic Relevance to Multiple Diseases Including COVID-19

Contact Info

Product

Resources

About