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Updated: Aug 8, 2025

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops

Walid Hamdy¹, Amr Ismail¹, Wael A Awad²

¹Faculty of Science, Port Said University, Port Said 42511, Egypt.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

This study introduces an optimized deep learning model for predicting plant microRNA (miRNA) and long non-coding RNA (lncRNA) interactions. The model achieves 97.7% accuracy, outperforming existing methods for enhanced plant breeding and stress response research.

Keywords:

CNN model IndRNN deep learning ensemble learning lncRNA microRNA plant

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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

Area of Science:

Plant molecular biology
Bioinformatics
Genomics

Background:

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are crucial regulatory molecules in plants, influencing growth and stress responses.
Predicting interactions between miRNAs and lncRNAs is vital for understanding gene regulation but is challenged by data limitations and noise.
Simple Sequence Repeat (SSR) markers offer new avenues for functional analysis in plant breeding.

Purpose of the Study:

To develop an accurate and reliable computational model for predicting plant miRNA-lncRNA interactions.
To address the limitations of traditional classification systems in handling small datasets and noise in biological sequence data.

Main Methods:

An optimized deep learning ensemble model combining Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) was proposed.
Hyperparameter tuning was performed using the artificial Gorilla Troops Algorithm and an intelligent preying algorithm.
IndRNN was utilized to capture sequence dependencies and features, overcoming traditional architecture limitations.

Main Results:

The proposed deep learning model achieved a prediction accuracy of 97.7% for plant miRNA-lncRNA interactions.
The model demonstrated superior performance compared to existing deep learning and shallow machine learning models, especially with large-scale and extended sequences.
The optimized hyperparameter tuning significantly enhanced prediction accuracy.

Conclusions:

The developed deep learning model offers a highly accurate method for predicting plant miRNA-lncRNA interactions.
This advancement has significant implications for plant breeding, genetic research, and understanding plant responses to environmental stresses.
The integration of IndRNN and advanced optimization algorithms provides a robust framework for analyzing non-coding RNA interactions.