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Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization.

Xing Chen1, Chi Zhou2, Chun-Chun Wang2

  • 1Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China.

Briefings in Bioinformatics
|August 17, 2021
PubMed
Summary
This summary is machine-generated.

We developed Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA), a computational model to identify potential small molecule (SM) and microRNA (miRNA) drug targets. BNNRSMMA accurately predicts SM-miRNA associations, aiding disease treatment discovery.

Keywords:
association predictionbounded nuclear norm regularizationmatrix completionmicroRNAsmall molecule

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Area of Science:

  • Computational Biology
  • Pharmacogenomics
  • Bioinformatics

Background:

  • MicroRNAs (miRNAs) are crucial targets for small molecule (SM) drugs in disease treatment.
  • Experimental identification of SM-miRNA associations is costly and time-consuming.
  • Computational models are essential for predicting potential SM-miRNA interactions.

Purpose of the Study:

  • To develop a novel computational model, Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA), for predicting SM-miRNA associations.
  • To improve the accuracy and reliability of predicting SM-miRNA interactions for drug discovery.

Main Methods:

  • Constructed a heterogeneous SM-miRNA network using miRNA similarity, SM similarity, and known associations.
  • Employed matrix completion by minimizing nuclear norm using the Alternating Direction Method of Multipliers.
  • Incorporated matrix element bounds (0,1) and a regularization term to enhance practical significance and noise tolerance.

Main Results:

  • BNNRSMMA achieved high predictive performance across various cross-validation methods (AUCs up to 0.9822 on Dataset 1).
  • Case studies confirmed numerous predicted SM-miRNA associations through experimental literature.
  • The model demonstrated robust performance on two independent datasets.

Conclusions:

  • BNNRSMMA is a reliable and effective computational tool for inferring small molecule-microRNA associations.
  • The model's predictions can significantly accelerate the discovery of novel therapeutic strategies.
  • The approach offers a cost-effective alternative to experimental screening for SM-miRNA interactions.