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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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mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.

Ru Nie^1,2, Zhengwei Li^3,4,5,6, Zhu-Hong You⁷

¹Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China.

BMC Medical Informatics and Decision Making

|August 31, 2021

Summary

This summary is machine-generated.

This study introduces HCFMDA, a new computational framework for predicting microRNA-disease associations. HCFMDA efficiently identifies potential disease-related microRNAs, saving time and resources compared to traditional methods.

Keywords:

Heterogeneous data Hybrid collaborative filtering Singular value decomposition miRNA-disease association prediction

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

MicroRNAs (miRNAs) are crucial in human complex diseases.
Traditional methods for identifying disease-related miRNAs are resource-intensive.

Purpose of the Study:

To develop an efficient computational framework for predicting miRNA-disease associations.
To integrate heterogeneous data for improved prediction accuracy.

Main Methods:

Developed HCFMDA framework integrating miRNA functional similarity, disease semantic similarity, and known miRNA-disease networks.
Utilized singular value decomposition for noise reduction and speed enhancement.
Fused similar disease, miRNA, and disease-miRNA associations to prioritize predictive scores.

Main Results:

Achieved an Area Under the Curve (AUC) of 0.8379 in leave-one-out cross-validation.
Validated predictions for Colon, Esophageal, and Prostate Neoplasms, with high confirmation rates (44-46/50).
Demonstrated applicability to diseases with no prior known miRNA associations, as shown in Breast Neoplasms case study.

Conclusions:

HCFMDA provides a reliable computational tool for identifying candidate miRNAs linked to human diseases.
The framework accelerates research and reduces experimental costs in miRNA-disease association studies.