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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 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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Missing value imputation for microRNA expression data by using a GO-based similarity measure.

Yang Yang1,2, Zhuangdi Xu3, Dandan Song4

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China. yangyang@cs.sjtu.edu.cn.

BMC Bioinformatics
|January 29, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to impute missing values in microRNA (miRNA) expression data by integrating functional similarity with Gene Ontology (GO) terms. This approach enhances data accuracy for biomarker discovery in diseases.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Missing values are common in microarray data, necessitating accurate imputation for reliable analysis.
  • Existing imputation methods for microRNA (miRNA) expression data often lack domain knowledge integration.
  • Developing domain knowledge-based imputation algorithms for miRNA profiles is crucial for improving data quality.

Purpose of the Study:

  • To develop a novel imputation method for miRNA expression profiles that incorporates biological domain knowledge.
  • To improve the accuracy of missing value imputation in miRNA microarray data.
  • To leverage Gene Ontology (GO) for defining miRNA functional similarity.

Main Methods:

  • Connected miRNAs to domain knowledge via their target genes using Gene Ontology (GO).
  • Defined miRNA functional similarity based on the semantic similarity of GO terms.
  • Developed a new measure combining miRNA functional and expression similarity for imputation.
  • Tested the imputation method on two breast cancer miRNA microarray datasets.

Main Results:

  • The proposed method achieved improved performance compared to expression-based imputation on both tested datasets.
  • Demonstrated that biological domain knowledge, specifically GO-based functional similarity, benefits miRNA data imputation.
  • The imputation accuracy of miRNA array data was enhanced by complementary functional information.

Conclusions:

  • Biological domain knowledge significantly improves missing value estimation in miRNA expression profiles.
  • Functional similarity derived from GO terms of target genes offers valuable complementary information for imputation.
  • The developed method enhances the accuracy of miRNA microarray data analysis for biomarker discovery.