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Related Concept Videos

MicroRNAs01:22

MicroRNAs

3.7K
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

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...
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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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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

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Unbiased microRNA-Disease Association Prediction Using ICD-11 Codes and Negative Sampling.

Munyoung Chang1,2,3, Jeonghee Jo4, Junyong Ahn5,6

  • 1Education and Research Program for Future ICT Pioneers, Seoul National University, Seoul, South Korea.

Pharmacology Research & Perspectives
|November 28, 2025
PubMed
Summary
This summary is machine-generated.

We developed Unbiased microRNA-disease association predictor (UBMDA) to predict microRNA-disease links. UBMDA uses disease codes and nucleotide sequences, enabling analysis of novel microRNAs and diseases.

Keywords:
associationconvolutional neural networkdiseasemicroRNAprediction

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • MicroRNA-disease associations are crucial for understanding disease mechanisms and developing targeted therapies.
  • Existing computational models often rely on similarity-based methods, limiting their applicability to novel or understudied microRNAs and diseases.
  • The lack of comprehensive negative sample datasets hinders accurate prediction model development.

Purpose of the Study:

  • To develop a novel computational model, Unbiased microRNA-disease association predictor (UBMDA), for predicting microRNA-disease associations.
  • To overcome limitations of previous methods by utilizing International Classification of Diseases (ICD-11) codes and microRNA nucleotide sequences as input features.
  • To construct a balanced negative sample dataset that accounts for potential biases in microRNA and disease frequencies.

Main Methods:

  • Developed UBMDA, a computational model employing ICD-11 disease codes and microRNA nucleotide sequences for feature extraction.
  • Created a negative sample dataset by carefully considering the frequencies of microRNAs and diseases present in the positive sample dataset to prevent prediction bias.
  • Implemented a strategy to ensure similar microRNA and disease frequencies between positive and negative sample datasets.

Main Results:

  • Successfully developed the UBMDA computational model with a simple and intuitive structure.
  • Demonstrated the model's capability to predict microRNA-disease associations without relying on similarity-based feature extraction.
  • The approach addresses the challenge of limited negative sample data in microRNA research.

Conclusions:

  • UBMDA offers a novel approach for predicting microRNA-disease associations, applicable to newly discovered or poorly characterized microRNAs and diseases.
  • The model's design, utilizing ICD-11 codes and nucleotide sequences, enhances its versatility and predictive power.
  • UBMDA is expected to accelerate the discovery of microRNA-related biomarkers and therapeutic strategies.