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MicroRNAs

MicroRNAs

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...

MicroRNAs

MicroRNAs

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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Updated: Mar 1, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

Published on: January 7, 2019

Handling High-Dimension (High-Feature) MicroRNA Data.

Yue Hu¹, Wenjun Lan², Daniel Miller³

¹College of Bioengineering, Qilu University of Technology, No. 3501, Da Xue Rd., Changqing District, Jinan, Shandong, 250353, People's Republic of China.

Methods in Molecular Biology (Clifton, N.J.)

|May 26, 2017

Summary

This summary is machine-generated.

High-dimensional data presents challenges in microRNA and microarray analysis. This study explores methods like degenerated k-mers and gapped k-mers to reduce dimensionality and improve statistical robustness for better data processing.

Keywords:

Degenerated k-mer Dimension decreasing Gapped k-mer High-dimension miRNA

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Last Updated: Mar 1, 2026

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

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Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-dimensional data, common in microRNA and microarray analysis, leads to the 'curse of dimensionality'.
Limited sample sizes exacerbate statistical errors with numerous variables.
Processing and interpreting high-feature variables pose significant challenges.

Purpose of the Study:

To address the 'curse of dimensionality' in biological sequence and microarray data.
To introduce and describe methods for reducing the number of variables.
To enhance the statistical robustness of data processing.

Main Methods:

A degenerated k-mer method was proposed for variable dimension reduction.
A gapped k-mer method was introduced to improve statistical robustness.
Traditional supervised and unsupervised mathematical methods for dimensionality reduction were also described.

Main Results:

The discussed methods aim to mitigate issues arising from high-dimensional biological data.
Degenerated and gapped k-mer approaches offer solutions for variable reduction and robustness.
Integration of traditional mathematical techniques further aids in data simplification.

Conclusions:

Dimensionality reduction is crucial for effective analysis of high-dimensional biological data.
Novel k-mer based methods and established mathematical techniques provide viable solutions.
These approaches enhance data processing efficiency and statistical reliability.