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Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition.

Jie Ni1, Xiaolong Cheng1, Tongguang Ni1

  • 1School of Computer Science and Artificial Intelligence and Aliyun School of Big Data and School of Software, Changzhou University, Changzhou, China.

Frontiers in Molecular Biosciences
|December 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces GCNLASMMA, a novel computational model for predicting associations between small molecule drugs and microRNAs. The method accurately identifies potential therapeutic relationships, aiding in understanding disease treatment.

Keywords:
association predictiondeep learningmatrix decompositionmicroRNAsmall molecule

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Predicting small molecule (SM) and microRNA (miRNA) associations is crucial for understanding SM drug mechanisms in miRNA-related diseases.
  • Traditional prediction methods are inefficient and struggle with noisy or unprioritized data.
  • Existing computational models often fail to effectively filter or rank known SM-miRNA associations.

Purpose of the Study:

  • To develop an accurate and efficient computational model for predicting potential associations between small molecule drugs and microRNAs.
  • To overcome limitations of existing methods in handling noisy data and prioritizing significant associations.
  • To enhance the understanding of how small molecule drugs can treat endogenous miRNA-related diseases.

Main Methods:

  • Proposed a Graph Convolutional Network with Layer Attention mechanism for SM-MiRNA Association prediction (GCNLASMMA).
  • Incorporated newly identified SM-miRNA associations (via matrix decomposition), integrated SM similarity, and miRNA similarity into a heterogeneous network.
  • Utilized a graph convolutional network with an attention mechanism to reconstruct the SM-miRNA association matrix.

Main Results:

  • GCNLASMMA demonstrated excellent performance across multiple cross-validation strategies, including global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV, and 5-fold cross-validation.
  • Case studies confirmed numerous hypothesized SM-miRNA associations through experimental literature.
  • The model proved to be a trustworthy method for inferring SM-miRNA associations.

Conclusions:

  • GCNLASMMA is a reliable and high-performing computational tool for predicting small molecule-microRNA associations.
  • The model's accuracy and validation suggest its utility in drug discovery and understanding disease mechanisms.
  • This approach offers a significant advancement over traditional and existing computational methods for SM-miRNA association prediction.