Predicting multiple types of associations between mirnas and diseases based on graph regularized weighted tensor decomposition

HIGHLIGHTS

• who: Associations Between miRNAs and colleagues from the Faculty of Information Technology, Macau University of Science and Technology, Macau, China, School of Mathematics and have published the Article: Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition, in the Journal: (JOURNAL)
• what: To address the aforementioned problems, in this article, the authors propose a computational framework named Weighted Tensor Decomposition with Auxiliary Information, Graph Laplacian regularization, and L2,1 Norm (WeightTDAIGN), which integrates weight, graph Laplacian regularization, L2,1 norm, and more auxiliary information into tensor decomposition to . . .
  

   

  

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