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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Related Experiment Video

Updated: Jan 31, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Sequence-based bacterial small RNAs prediction using ensemble learning strategies.

Guifeng Tang¹, Jingwen Shi², Wenjian Wu³

¹School of Computer Science, Wuhan University, Wuhan, 430072, China.

BMC Bioinformatics

|December 23, 2018

Summary

This summary is machine-generated.

Accurate prediction of bacterial small non-coding RNAs (sRNAs) is crucial for understanding bacterial mechanisms. New ensemble learning methods, weighted average ensemble method (WAEM) and neural network ensemble method (NNEM), show improved prediction accuracy.

Keywords:

Ensemble learning Neural network Sequence-derived feature Small RNA prediction

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Bacterial small non-coding RNAs (sRNAs) regulate key physiological processes.
Accurate prediction of sRNAs is essential for functional and mechanistic studies.

Purpose of the Study:

To develop accurate ensemble learning methods for predicting bacterial sRNAs.
To explore the utility of various sequence-derived features in sRNA prediction.

Main Methods:

Utilized diverse sRNA sequence-derived features: spectrum profile, mismatch profile, reverse complement k-mer, and pseudo nucleotide composition.
Developed two ensemble learning strategies: Weighted Average Ensemble Method (WAEM) and Neural Network Ensemble Method (NNEM).
Evaluated methods using balanced and imbalanced datasets with 5-fold cross-validation.

Main Results:

Both WAEM and NNEM demonstrated superior performance compared to existing state-of-the-art sRNA prediction methods.
Ensemble learning effectively integrated diverse sequence features for enhanced prediction accuracy.
The methods were validated on multiple datasets, confirming their robustness.

Conclusions:

WAEM and NNEM offer significant potential for accurate bacterial sRNA prediction.
These methods can aid in elucidating the biological mechanisms of bacteria.