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Related Experiment Video

Updated: Aug 27, 2025

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Predicting miRNA-lncRNA interactions on plant datasets based on bipartite network embedding method.

Linlin Zhuo¹, Shiyao Pan¹, Jing Li¹

¹School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang 325035, China.

Methods (San Diego, Calif.)

|September 26, 2022

Summary

This study introduces a novel method for predicting plant microRNA-lncRNA interactions (MLIs) using bipartite graphs. The approach enhances understanding of MLIs in plants, crucial for microbiology and disease research.

Keywords:

Bipartite graph Link prediction Network embedding miRNA-lncRNA interactions

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Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

MicroRNA-lncRNA interactions (MLIs) are critical in microbiology and disease.
Current research on MLIs predominantly focuses on animals, with limited comprehensive studies on plants.

Purpose of the Study:

To address the gap in plant microRNA-lncRNA interaction (MLI) prediction.
To develop and validate a robust link prediction method for plant MLIs.

Main Methods:

Utilized a bipartite graph approach for MLI link prediction.
Extracted and processed attribute and structure information for network embedding.
Developed a loss function incorporating intra-partition and inter-partition proximity modeling.

Main Results:

Achieved encouraging performance in link prediction tasks on plant datasets.
Demonstrated the superiority of the proposed approach through experiments.
Validated the significance of the method for plant MLI research.

Conclusions:

The developed method effectively predicts plant microRNA-lncRNA interactions.
This work contributes significantly to the understanding of MLIs in plants and their implications for microbiology and disease.