Skip to main content
padlock icon - secure page this page is secure

Prediction of MicroRNA–disease Associations by Matrix Completion

Buy Article:

$68.00 + tax (Refund Policy)

Background: MicroRNAs play important roles in the progression of various diseases. Therefore, it is of vital importance to predict novel microRNA-disease associations for understanding disease mechanisms.

Objective: As far as we see, there are generally three problems for the microRNA-disease association prediction. The first one is the lack of similarity among miRNAs. The second one is the presence of a few defined relationships between miRNAs and diseases. The insufficient number of available negative samples for studies on miRNA–disease associations is another troubling issue. We aimed to solve the three problems with the inductive matrix completion method.

Method: In this paper, the inductive matrix completion method is exploited to overcome the three problems. We also contributed multiple feature sets to address problems related to insufficient miRNA–disease association data. The method could be applied to predict unknown microRNA-disease associations and new pathogenic miRNAs for well-characterized diseases.

Results: Experiments can prove the performance of our inductive matrix completion method. The experiment is compared with several current methods through cross-validation. Our result reveals the superiority of our method to other approaches.

Conclusion: We can conclude that the inductive matrix completion method is more suitable than transductive one, for the prediction of microRNA-disease associations.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: biological network; inductive matrix completion; microRNA–disease association prediction

Document Type: Research Article

Publication date: June 1, 2016

More about this publication?
  • Current Proteomics research in the emerging field of proteomics is growing at an extremely rapid rate. The principal aim of Current Proteomics is to publish well-timed review articles in this fast-expanding area on topics relevant and significant to the development of proteomics. Current Proteomics is an essential journal for everyone involved in proteomics and related fields in both academia and industry.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more