A clustering-based sampling method for mirna-disease association prediction

HIGHLIGHTS

• who: September and collaborators from the Harvard Medical School, United States Changzhou University, China have published the research work: A clustering-based sampling method for miRNA-disease association prediction, in the Journal: (JOURNAL)
• what: The authors implemented an ensemble learning framework for miRNA-disease association prediction. The authors employed five-fold cross-validation to evaluate the performance of the CSMDA. The authors compared the performance of four base classifiers: AdaBoost, Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtRa Trees).
• how: A set of base classifiers were trained on the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.