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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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mirMachine: A One-Stop Shop for Plant miRNA Annotation

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A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods.

Zheng-Xing Guan¹, Shi-Hao Li¹, Zi-Mei Zhang¹

¹Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu610054, China.

Current Genomics

|July 14, 2020

Summary

This summary is machine-generated.

Accurate identification of microRNA precursors (pre-miRNAs) is crucial for understanding gene regulation and disease. Machine learning methods offer an efficient alternative to traditional techniques for pre-miRNA recognition.

Keywords:

benchmark dataset feature extraction identification machine learning methods microRNA precursor prediction algorithm

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Area of Science:

Bioinformatics
Molecular Biology
Computational Biology

Background:

MicroRNAs (miRNAs) are short non-coding RNA molecules regulating gene expression.
Aberrant miRNA expression is linked to various diseases.
Accurate identification of miRNA precursors (pre-miRNAs) is essential for biological and medical research.

Purpose of the Study:

To review computational methods for pre-miRNA recognition.
To summarize advances in benchmark datasets, feature extraction, and prediction algorithms.
To provide an overview of available pre-miRNA predictors and future research directions.

Main Methods:

Review of experimental, comparative genomics, and artificial intelligence (AI) methods for pre-miRNA identification.
Focus on machine learning-based approaches due to their efficiency.
Analysis of benchmark datasets, feature extraction techniques, and prediction algorithms.

Main Results:

Machine learning methods are more efficient than experimental and comparative genomics approaches for pre-miRNA identification.
Summary of current advances in computational pre-miRNA recognition.
Information on available pre-miRNA predictors is provided.

Conclusions:

Computational methods, particularly machine learning, represent a promising avenue for efficient pre-miRNA identification.
This review offers a comprehensive background for researchers in the field.
Future perspectives highlight ongoing advancements in pre-miRNA recognition technology.