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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...

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MiRNA-disease association prediction based on meta-paths.

Liang Yu¹, Yujia Zheng¹, Lin Gao¹

¹School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China.

Briefings in Bioinformatics

|January 12, 2022

Summary

This study introduces a novel method for predicting microRNA-disease associations using meta-paths, achieving high accuracy. The model effectively identifies potential microRNA biomarkers for various cancers, even for diseases with limited prior data.

Keywords:

association prediction disease graph convolutional network heterogeneous information network meta-path miRNA

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

MicroRNAs (miRNAs) are crucial regulators of gene expression with potential as diagnostic biomarkers and therapeutic targets.
Accurate prediction of miRNA-disease associations is vital for advancing precision medicine and drug discovery.
Existing methods face challenges in capturing complex relationships within biological networks.

Purpose of the Study:

To develop and validate a novel computational method, miRNA-disease association prediction based on meta-paths (MDPBMP), for identifying potential miRNA-disease associations.
To construct a heterogeneous information network integrating miRNAs, diseases, and genes to model their intricate relationships.
To assess the predictive performance and clinical relevance of the proposed MDPBMP method.

Main Methods:

Construction of an miRNA-disease-gene heterogeneous information network.
Definition and application of seven symmetrical meta-paths to capture diverse semantic relationships.
Feature vector extraction and aggregation from meta-path instances to update node embeddings.
Calculation of miRNA-disease association scores using aggregated feature vectors.

Main Results:

The MDPBMP method achieved a superior Area Under the Curve (AUC) of 0.9214, outperforming existing approaches.
High validation rates were observed for predicted miRNAs across lung, esophageal, colon, and breast neoplasms (49%, 48%, 49%, and 50% respectively).
The model demonstrated robust performance in predicting potential miRNAs for diseases with no prior known associations, as shown in the breast neoplasm case study.

Conclusions:

The MDPBMP method provides an effective and accurate approach for predicting miRNA-disease associations.
The findings highlight the potential of miRNAs as biomarkers for cancer diagnosis and therapeutic targets.
The model's ability to predict novel associations underscores its utility in uncovering previously unknown etiological links between miRNAs and diseases.