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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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A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.

Qiu Xiao1, Jiawei Luo1, Cheng Liang2

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.

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|October 3, 2017
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Summary
This summary is machine-generated.

This study introduces GRNMF, a novel computational method for identifying potential microRNA (miRNA) and disease associations. GRNMF effectively discovers new links, especially for novel diseases and miRNAs, outperforming existing approaches.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) are key regulators in cellular processes.
  • Identifying disease-associated miRNAs offers insights into disease pathogenesis.
  • Existing methods struggle with novel or sparsely associated miRNAs and diseases.

Purpose of the Study:

  • To develop a computational method for discovering novel miRNA-disease associations.
  • To address limitations of existing approaches biased towards known associations.
  • To improve identification of associations for new or under-represented miRNAs and diseases.

Main Methods:

  • Proposed Graph Regularized Non-negative Matrix Factorization (GRNMF) method.
  • Integrated disease semantic and miRNA functional information for similarity estimation.
  • Developed a preprocessing step to construct interaction score profiles for novel entities.
  • Utilized a graph regularized non-negative matrix factorization framework for simultaneous prediction.

Main Results:

  • GRNMF effectively prioritizes disease-associated miRNAs with high accuracy.
  • The method outperforms recent computational approaches.
  • Case studies validated GRNMF's effectiveness in inferring unknown miRNA-disease associations for novel diseases and miRNAs.

Conclusions:

  • GRNMF offers a robust framework for discovering novel miRNA-disease associations.
  • The method is particularly valuable for exploring associations involving new or under-characterized miRNAs and diseases.
  • GRNMF enhances our understanding of miRNA roles in disease pathogenesis.