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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 the pre-miRNA...

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...

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Updated: Feb 20, 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

LEMRG: Decision Rule Generation Algorithm for Mining MicroRNA Expression Data.

Łukasz Piątek^1,2, Jerzy W Grzymała-Busse^3,4

¹Institute für Biomedizinische Technik und Informatik, Technische Universität Ilmenau, Gustav-Kirchoff 2 Str, 98684, Ilmenau, Germany. lukasz.piatek@tu-ilmenau.de.

Advances in Experimental Medicine and Biology

|October 24, 2017

Summary

This summary is machine-generated.

This study introduces a new algorithm, LEMRG, for analyzing microRNA (miRNA) expression data. LEMRG improves classification accuracy and stability for cancer diagnosis by generating multiple decision rules, outperforming traditional methods.

Keywords:

AQ Cumulative decision rule sets Data mining Decision rule generations GTS Induction of decision rules LEM2 LEMRG MLEM2 MiRNA MicroRNA

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Last Updated: Feb 20, 2026

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Area of Science:

Bioinformatics
Machine Learning
Genomics

Background:

MicroRNA (miRNA) expression data analysis is crucial for gene regulation understanding.
Microarray data presents challenges due to a small number of samples and a large number of attributes, hindering traditional machine learning approaches.
Existing methods often struggle with classifier instability and accuracy when dealing with high-dimensional, low-sample-size data.

Purpose of the Study:

To develop a generalized algorithm for mining microarray data, specifically miRNA expression datasets.
To enhance the stability and accuracy of classification for newly created learning classifiers.
To address the limitations of traditional machine learning in handling the unique characteristics of microarray data.

Main Methods:

Developed the Learning from Examples Module based on Rule Generations (LEMRG) algorithm.
Employed an iterative approach to induce multiple decision rule sets (decision rule generations) instead of a single set.
Utilized miRNA expression data for 11 types of human cancers for training and 4 types for testing.

Main Results:

The novel LEMRG algorithm demonstrated improved stability and classification accuracy compared to traditional single decision rule sets.
LEMRG's cumulative decision rule sets achieved classification effectiveness comparable to only 3 out of 16 other tested machine learning models.
Accurate classification of colon and ovary cancer cases was achieved, and specific miRNAs were identified as potential diagnostic biomarkers.

Conclusions:

The LEMRG algorithm offers a robust solution for analyzing complex miRNA expression data.
Iterative generation of decision rules enhances classifier performance in high-dimensional, low-sample-size scenarios.
The identified miRNAs hold promise as biomarkers for early cancer diagnosis.