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Related Concept Videos

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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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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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An improved random forest-based computational model for predicting novel miRNA-disease associations.

Dengju Yao1, Xiaojuan Zhan2, Chee-Keong Kwoh3

  • 1School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, 150080, China. ydkvictory@hrbust.edu.cn.

BMC Bioinformatics
|December 5, 2019
PubMed
Summary

This study introduces IRFMDA, an improved computational model for identifying microRNA (miRNA)-disease associations. IRFMDA accurately predicts disease-related miRNAs, aiding in understanding disease pathogenesis and treatment.

Keywords:
DiseaseFeature selectionRandom forestmiRNAmiRNA-disease association prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) regulate gene expression post-transcriptionally; their dysregulation is linked to complex human diseases.
  • Accurate identification of disease-associated miRNAs is crucial for understanding disease mechanisms and developing treatments.
  • Computational models offer an economical and effective alternative to time-consuming and expensive experimental methods for predicting miRNA-disease associations.

Purpose of the Study:

  • To develop an improved computational model, IRFMDA (Identifying miRNA-Disease Associations), for predicting associations between miRNAs and diseases.
  • To enhance the accuracy of miRNA-disease association prediction by integrating multiple similarity measures and employing feature selection.

Main Methods:

  • Integrated disease similarity was calculated using semantic and Gaussian interaction profile kernel (GIPK) similarity.
  • Integrated miRNA similarity was computed using functional and GIPK similarity.
  • A random forest (RF) model was trained using integrated similarities, incorporating feature selection based on variable importance scores for optimizing prediction.

Main Results:

  • The IRFMDA model achieved high performance with AUCs of 0.8728 (local LOOCV), 0.9398 (global LOOCV), and 0.9363 (5-fold CV), outperforming existing models.
  • Case studies on esophageal cancer, lymphoma, and lung cancer showed high validation rates for predicted miRNA-disease associations against experimental data.
  • IRFMDA successfully identified 94, 98, and 100 top-ranked disease-associated miRNAs for esophageal cancer, lymphoma, and lung cancer, respectively, supported by dbDEMC v2.0.

Conclusions:

  • IRFMDA demonstrates excellent performance as a miRNA-disease association prediction model.
  • The model's accuracy and validation suggest its utility in guiding future experimental research.
  • IRFMDA can provide valuable insights into the regulatory roles of miRNAs in complex human diseases.