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Updated: Jan 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

miPIE: NGS-based Prediction of miRNA Using Integrated Evidence.

R J Peace¹, M Sheikh Hassani¹, J R Green²

¹Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada.

Scientific Reports

|February 9, 2019

Summary

This summary is machine-generated.

We developed microRNA Prediction using Integrated Evidence (miPIE), a novel method for de novo microRNA identification. miPIE integrates expression and sequence features, significantly improving prediction accuracy over existing tools.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

De novo microRNA identification methods often rely on sequence features.
Next-generation sequencing (NGS) provides transcriptome data with expression patterns.
Existing tools like miRDeep2 and miRanalyzer have limitations in integrating diverse features.

Purpose of the Study:

To develop a novel method for de novo microRNA identification.
To integrate both expression-based and sequence-based features for improved prediction.
To address the limitations of current de novo microRNA prediction tools.

Main Methods:

Developed microRNA Prediction using Integrated Evidence (miPIE).
Integrated NGS transcript expression patterns with advanced genomic sequence-based features.
Performed feature selection, identifying 20 discriminative features, including 3 expression-based ones.

Main Results:

miPIE demonstrated significantly improved miRNA prediction performance.
Evaluated using precision-recall curves across six diverse species NGS datasets.
Outperformed miRDeep2, miRanalyzer, and mirnovo in prediction accuracy.

Conclusions:

Combining expression-based and sequence-based features is more effective for miRNA prediction than using either alone.
miPIE offers a more robust approach to de novo microRNA identification.
The developed method advances the integration of multi-modal data in bioinformatics.