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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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EMCMDA: predicting miRNA-disease associations via efficient matrix completion.

Chao Qin1, Jiancheng Zhang2, Lingyu Ma3

  • 1School of Information Science and Engineering, Qilu Normal University, Jinan, 250200, China. 20155612@qlnu.edu.cn.

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|June 4, 2024
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Summary

This study introduces efficient matrix completion (EMCMDA), a novel computational method for predicting microRNA (miRNA)-disease associations. EMCMDA accurately identifies novel disease-related miRNAs, offering a faster alternative to traditional experimental validation.

Keywords:
Heterogeneous information networkMatrix completionMiRNA-disease associationsMulti-source similarityTruncated schatten p-norm

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Area of Science:

  • Genomics
  • Computational Biology
  • Biomedical Informatics

Background:

  • MicroRNAs (miRNAs) are critical regulators of biological processes.
  • miRNAs show potential as therapeutic targets for complex diseases.
  • Experimental validation of miRNA-disease associations is costly and time-consuming.

Purpose of the Study:

  • To develop an efficient computational method for predicting miRNA-disease associations.
  • To overcome the limitations of traditional experimental validation methods.
  • To identify novel miRNA-disease relationships.

Main Methods:

  • Developed an efficient matrix completion approach (EMCMDA).
  • Integrated multi-source miRNA and disease similarity measures.
  • Constructed a heterogeneous network and derived a target matrix.
  • Employed a weighted singular value contraction technique for matrix completion.

Main Results:

  • EMCMDA demonstrated statistically significant performance in cross-validation experiments on two databases.
  • Case studies on lung and breast cancer revealed EMCMDA's ability to predict previously unknown disease-related miRNAs.
  • The weighted singular value contraction improved upon conventional algorithms.

Conclusions:

  • EMCMDA is a robust and effective computational tool for miRNA-disease association prediction.
  • The method offers a valuable alternative to experimental validation, accelerating discovery.
  • EMCMDA accurately forecasts novel miRNA-disease links, aiding therapeutic target identification.