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mirMachine: A One-Stop Shop for Plant miRNA Annotation

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Published on: May 1, 2021

A knowledge-driven network for fine-grained relationship detection between miRNA and disease.

Shengpeng Yu¹, Hong Wang¹, Tianyu Liu¹

¹School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.

Briefings in Bioinformatics

|March 24, 2022

Summary

This summary is machine-generated.

This study introduces KDFGMDA, a novel computational approach for predicting fine-grained associations between microRNAs (miRNAs) and diseases. KDFGMDA accurately identifies specific relationships like upregulation or downregulation, aiding disease diagnosis and treatment.

Keywords:

fine-grained relationship knowledge-driven network miRNA and disease

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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

Area of Science:

Computational biology
Genomics
Biomedical informatics

Background:

MicroRNAs (miRNAs) are crucial in disease pathogenesis, especially cancer.
Identifying disease-related miRNAs is vital for clinical diagnosis and treatment.
Current computational methods lack fine-grained prediction of miRNA-disease associations.

Purpose of the Study:

To develop a knowledge-driven approach (KDFGMDA) for fine-grained prediction of disease-related miRNAs.
To accurately predict specific associations (e.g., upregulation, downregulation, dysregulation) between miRNAs and diseases.
To enhance the clinical utility of miRNA research in disease diagnosis and treatment.

Main Methods:

Constructing a knowledge graph by extracting triple information from experimental data and existing datasets.
Training a deep graph representation learning model on the knowledge graph.
Utilizing KDFGMDA for fine-grained prediction of miRNA-disease relationships.

Main Results:

KDFGMDA accurately predicts miRNA-disease relationships at a fine-grained level.
Experimental results demonstrate the method's effectiveness in identifying potential candidate miRNAs.
Case studies on cancers, survival analysis, and expression analysis validate KDFGMDA's feasibility.

Conclusions:

KDFGMDA offers a significant advancement over existing methods for miRNA-disease association prediction.
The approach has far-reaching implications for medical clinical research, early diagnosis, and disease management.
KDFGMDA provides a powerful tool for uncovering detailed miRNA-disease interactions crucial for therapeutic development.