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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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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

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Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.

K Deepthi^1,2, A S Jereesh³

¹Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, 682022, Kerala, India. deepthi523@gmail.com.

Molecular Genetics and Genomics : MGG

|June 26, 2020

Summary

This study introduces a computational method, bilateral-inductive matrix completion (BIMC), to predict drug-disease relationships by analyzing microRNA targets. The approach effectively identifies potential drug repositioning candidates and causal microRNAs for diseases.

Keywords:

Association Disease Drug repositioning Inductive matrix MiRNA

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Identifying causal mechanisms for drug-disease associations is complex.
MicroRNAs (miRNAs) are key players in human diseases.
Drug repositioning offers a cost-effective alternative to traditional drug discovery.

Purpose of the Study:

To develop a computational method for predicting drug-disease relationships via target miRNAs.
To facilitate drug repositioning by identifying novel drug-disease associations.
To predict causal miRNAs involved in disease mechanisms.

Main Methods:

Bilateral-inductive matrix completion (BIMC) algorithm applied to drug-miRNA and miRNA-disease association matrices.
Integration of drug, miRNA, and disease similarities as side information.
Leave-one-out cross-validation (LOOCV) for performance evaluation.

Main Results:

Achieved Area Under the Curve (AUC) scores of 0.792 for drug-disease, 0.759 for drug-miRNA, and 0.791 for miRNA-disease predictions.
Demonstrated superior prediction ability and robustness compared to previous models.
Successfully predicted novel drug-disease relationships and potential causal miRNAs.

Conclusions:

The BIMC approach is effective for predicting drug-disease associations and identifying causal miRNAs.
The method supports drug repositioning by highlighting promising drug-disease candidates.
Predicted relationships warrant further biological validation and experimental testing.