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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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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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MicroRNAs01:22

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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...
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Related Experiment Video

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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
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Identifying human microRNA-disease associations by a new diffusion-based method.

Bo Liao1, Sumei Ding1, Haowen Chen1

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

Journal of Bioinformatics and Computational Biology
|May 26, 2015
PubMed
Summary

This study introduces a novel computational method, NDBM, to predict microRNA-disease relationships. NDBM significantly improves prediction accuracy, aiding disease pathogenesis research.

Keywords:
MicroRNA–disease associationdiffusion-based methodnetwork similarity

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Identifying microRNA-disease relationships is crucial for understanding disease pathogenesis.
  • Experimental validation of disease-related microRNAs is challenging due to frequent new discoveries.
  • Computational methods for microRNA-disease prediction are gaining importance.

Purpose of the Study:

  • To develop a novel computational method for predicting microRNA-disease associations.
  • To improve the accuracy and efficiency of identifying disease-related microRNAs.
  • To provide a tool applicable to novel microRNAs and diseases with limited known associations.

Main Methods:

  • Constructed a microRNA (miRNA) functional network and a disease similarity network using integrated information sources.
  • Introduced a new diffusion-based method (NDBM) to explore global network similarity.
  • Employed leave-one-out cross-validation for performance evaluation.

Main Results:

  • NDBM achieved an Area Under the ROC Curve (AUC) of 85.62%, significantly outperforming previous methods.
  • The method demonstrated effectiveness for diseases with no known related microRNAs and for novel microRNAs.
  • Several predicted associations were confirmed by public databases.

Conclusions:

  • NDBM is an effective computational tool for inferring microRNA-disease associations.
  • The method enhances prediction accuracy and broadens applicability in biomedical research.
  • NDBM facilitates the identification of novel microRNA-disease links, aiding disease pathogenesis studies.