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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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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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Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
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A Meta-Path-Based Prediction Method for Human miRNA-Target Association.

Jiawei Luo1, Cong Huang2, Pingjian Ding2

  • 1College of Information Science and Electronic Engineering & Collaboration and Innovation Center for Digital Chinese Medicine of 2011 Project of Colleges and Universities in Hunan Province, Hunan University, Changsha, Hunan 410082, China.

Biomed Research International
|October 6, 2016
PubMed
Summary
This summary is machine-generated.

Researchers developed novel methods, RMLM and RMLMSe, to predict microRNA (miRNA)-target associations using genomic data. RMLMSe improves prediction by integrating sequence information, enhancing understanding of gene regulation and disease development.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • MicroRNAs (miRNAs) are crucial regulators of gene expression, with dysregulation linked to development and cancer.
  • Predicting miRNA-target interactions is vital but challenging, as existing methods often underutilize validated experimental data.
  • Accurate identification of miRNA-mRNA interactions is essential for understanding gene regulation.

Purpose of the Study:

  • To develop novel computational methods for predicting miRNA-target associations.
  • To leverage experimentally validated interactions and integrate diverse genomic data, including sequence information.
  • To improve the accuracy and comprehensiveness of miRNA-target prediction.

Main Methods:

  • Developed RMLM (Relational Matrix Learning Model) and RMLMSe (RMLM with Sequence information).
  • Utilized RM measure and meta-paths to evaluate miRNA-target relatedness.
  • Employed logistic regression and Maximum Likelihood Estimation (MLE) for weight estimation; integrated sequence data in RMLMSe.
  • Performed fivefold cross-validation and pathway enrichment analysis.

Main Results:

  • RMLM and RMLMSe demonstrated global prediction capabilities, reconstructing missing miRNA-target associations.
  • RMLMSe significantly improved prediction performance compared to RMLM by integrating sequence information.
  • Methods achieved higher AUC scores than existing approaches in fivefold cross-validation experiments.
  • Pathway enrichment analysis validated the biological relevance of predicted associations.

Conclusions:

  • RMLM and RMLMSe are effective global approaches for predicting miRNA-target associations.
  • Integrating sequence information enhances the accuracy of miRNA-target prediction.
  • These methods offer valuable tools for bioinformatics research in gene regulation and disease studies.