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MicroRNAs

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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

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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 ends...

MicroRNAs

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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 ends...

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Related Experiment Video

Updated: May 29, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

Ramkrishna Mitra¹, Sanghamitra Bandyopadhyay

¹Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India.

|September 28, 2011

Summary

This summary is machine-generated.

This study introduces MultiMiTar, an improved microRNA-target prediction system. MultiMiTar enhances accuracy and reliability by using high-quality data and advanced feature selection for biologists.

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Last Updated: May 29, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

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Published on: May 1, 2021

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Published on: May 25, 2015

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Machine learning miRNA-target prediction faces challenges with accuracy, feature selection, and prioritizing true positives.
Existing algorithms struggle with balanced sensitivity/specificity and effective prediction of translational repression.

Purpose of the Study:

To develop an efficient and reliable microRNA-target prediction system.
To address limitations in current algorithms regarding accuracy, feature selection, and prediction ranking.

Main Methods:

Developed MultiMiTar, a Support Vector Machine (SVM) based classifier.
Integrated a multi-objective metaheuristic feature selection technique (AMOSA-SVM).
Utilized high-quality negative examples and biologically relevant features.

Main Results:

MultiMiTar achieved a Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 on independent test data.
Outperformed existing methods with significantly higher MCC and ACA values.
Demonstrated balanced precision and sensitivity for translationally repressed targets.

Conclusions:

MultiMiTar offers improved reliability and accuracy for microRNA-target prediction.
The system preferentially ranks true positive predictions, aiding biological interpretation.
MultiMiTar is available as an online tool and downloadable software.