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Related Experiment Video

Updated: Jun 4, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Predicting noncoding RNA and disease associations using multigraph contrastive learning.

Si-Lin Sun^1,2, Yue-Yi Jiang^1,2, Jun-Ping Yang^1,2

¹College of Information Science Technology, Hainan Normal University, Haikou, 571158, China.

Scientific Reports

|January 2, 2025

Summary

This summary is machine-generated.

This study introduces K-MGCMLD, a novel deep learning method for predicting associations between noncoding RNAs (miRNAs, lncRNAs) and diseases. It achieves high accuracy in identifying these crucial biological relationships for improved diagnostics.

Keywords:

Diseases Graph contrastive learning Heterogeneous graph MiRNAs Multi-association prediction lncRNAs

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

Area of Science:

Computational biology
Bioinformatics
Artificial intelligence in medicine

Background:

Noncoding RNAs, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), play critical roles in biological processes.
Accurate prediction of noncoding RNA-disease associations is vital for early disease diagnosis and understanding disease mechanisms.
Existing deep learning methods often suffer from low prediction accuracy and are limited to single RNA type-disease associations.

Purpose of the Study:

To develop an advanced deep learning model, K-Means and multigraph Contrastive Learning for predicting associations among miRNAs, lncRNAs, and diseases (K-MGCMLD).
To overcome limitations of existing methods by improving prediction accuracy and enabling simultaneous prediction of multiple noncoding RNA-disease associations.

Main Methods:

Constructed a heterogeneous graph integrating miRNAs, lncRNAs, and diseases.
Employed K-means clustering for down-sampling to balance positive and negative samples.
Utilized a Graph Convolutional Network (GCN) encoder and multigraph contrastive learning for feature extraction and capturing topological features.
Applied an XGBoost classifier for multi-association classification prediction using reconstructed features.

Main Results:

Achieved high Area Under the Curve (AUC) values: 0.9542 for miRNA-disease, 0.9603 for lncRNA-disease, and 0.9687 for lncRNA-miRNA associations.
Case analyses validated the top 30 predicted miRNA associations for lung cancer and Alzheimer's disease, demonstrating practical utility.

Conclusions:

K-MGCMLD effectively predicts multiple noncoding RNA-disease associations with high accuracy.
The proposed method offers a significant advancement in computational approaches for disease association prediction.
Validated predictions highlight the potential of K-MGCMLD in identifying disease-related noncoding RNAs for clinical applications.