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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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mirMachine: A One-Stop Shop for Plant miRNA Annotation
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MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

Xing Chen1, Ya-Wei Niu2, Guang-Hui Wang3

  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China. xingchen@cumt.edu.cn.

Journal of Translational Medicine
|December 14, 2017
PubMed
Summary

This study introduces MKRMDA, a computational model for predicting microRNA-disease associations. The model demonstrates reliable prediction ability, aiding in disease prevention and diagnosis.

Keywords:
DiseaseKronecker regularized least squaresMultiple kernel learningmiRNAmiRNA–disease association

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNA (miRNA) research reveals associations with complex human diseases.
  • Predicting miRNA-disease links is crucial for disease prevention, diagnosis, and treatment.
  • Computational methods offer a feasible approach to identify potential miRNA-disease associations.

Purpose of the Study:

  • To develop a novel computational model for predicting miRNA-disease associations.
  • To automatically optimize kernel combinations for enhanced prediction accuracy.
  • To identify potential miRNA-disease relationships for further biomedical research.

Main Methods:

  • Developed a multiple kernels learning-based Kronecker regularized least squares model (MKRMDA).
  • The model optimizes the combination of multiple kernels for both diseases and miRNAs.
  • Utilized cross-validation and case studies for performance evaluation.

Main Results:

  • MKRMDA achieved high AUCs in global (0.9040) and local (0.8446) leave-one-out cross-validation.
  • Fivefold cross-validation yielded an average AUC of 0.8894 ± 0.0015.
  • Case studies on cancers showed high validation rates (76-94%) for predicted miRNA-disease associations.

Conclusions:

  • The MKRMDA model exhibits reliable prediction capabilities for miRNA-disease associations.
  • The findings support MKRMDA's utility in advancing the field of miRNA research.
  • The model is expected to facilitate future investigations by biomedical researchers.