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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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Graph regularized L_2,1-nonnegative matrix factorization for miRNA-disease association prediction.

Zhen Gao¹, Yu-Tian Wang¹, Qing-Wen Wu¹

¹School of Software, Qufu Normal University, Qufu, 273165, China.

BMC Bioinformatics

|February 20, 2020

Summary

This summary is machine-generated.

This study introduces a novel computational framework, graph Laplacian regularized L2,1-nonnegative matrix factorization (GRL2,1-NMF), to identify microRNAs linked to human diseases. The method effectively predicts potential disease-related microRNAs, even those without prior known associations.

Keywords:

Disease NMF L 2, 1-norm miRNA miRNA-disease associations

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Aberrant microRNA expression is linked to numerous human diseases.
Computational models are crucial for studying disease mechanisms.

Purpose of the Study:

To develop a computational framework for inferring human disease-connected microRNAs.
To identify novel microRNA-disease associations.

Main Methods:

Integrated manually validated disease-microRNA data.
Calculated microRNA functional similarity and disease semantic similarities.
Measured Gaussian interaction profile (GIP) kernel similarities.
Employed weighted K nearest known neighbours (WKNKN) for data preprocessing.
Utilized graph Laplacian regularized L2,1-nonnegative matrix factorization (GRL2,1-NMF) for prediction.

Main Results:

The GRL2,1-NMF framework successfully predicted links between microRNAs and diseases.
The method demonstrated high predictive accuracy.

Conclusions:

GRL2,1-NMF effectively discovers potential disease-related microRNAs.
The framework shows strong performance in cross-validation, including for microRNAs with no previously known associated diseases.