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Related Concept Videos

MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
MicroRNAs01:22

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

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Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization.

Shudong Wang1, Chuanru Ren1, Yulin Zhang2

  • 1College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580 Shandong, China.

Briefings in Bioinformatics
|September 6, 2023
PubMed
Summary

A new computational model, RPCAΓNR, accurately predicts associations between small molecule (SM) drugs and microRNAs (miRNAs). This advances drug development by efficiently identifying potential therapeutic targets for complex diseases.

Keywords:
association predictionaugmented lagrange multiplier methodmicroRNArobust principal component analysissmall molecule

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Pharmacogenomics

Background:

  • MicroRNA (miRNA) dysregulation is linked to refractory diseases.
  • Identifying small molecule (SM)-miRNA associations aids clinical treatment.
  • Current computational methods for SM-miRNA association prediction lack accuracy and efficiency.

Purpose of the Study:

  • To develop a novel and efficient computational model for predicting SM-miRNA associations.
  • To improve the accuracy and robustness of existing prediction techniques.
  • To facilitate drug development and clinical treatment strategies.

Main Methods:

  • Developed RPCAΓNR, a robust principal component analysis (PCA) framework using γ-norm and l2,1-norm regularization.
  • Employed an Augmented Lagrange Multiplier method for model optimization and deriving association scores.
  • Utilized Gaussian Interaction Profile Kernel Similarity to capture SM and miRNA similarity in known associations.

Main Results:

  • RPCAΓNR demonstrated superior performance over state-of-the-art models in accuracy, efficiency, and robustness.
  • Extensive evaluations, including cross-validation, independent validation, and case studies, confirmed the model's effectiveness.
  • The model successfully streamlined the identification of SM-miRNA associations.

Conclusions:

  • RPCAΓNR offers a significant advancement in predicting SM-miRNA associations.
  • The model's efficiency and accuracy contribute to accelerating drug development.
  • This work provides valuable insights for treating complex human diseases through targeted therapies.