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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in

Xin-Fei Wang¹, Lan Huang¹, Yan Wang¹

¹Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Journal of Chemical Information and Modeling

|June 12, 2025

Summary

This summary is machine-generated.

We developed NCRAE, a novel framework for predicting cancer biomarkers using competitive endogenous RNA (ceRNA) networks. This method enhances prediction accuracy, especially in noisy biological data, by learning robust node embeddings.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Competitive endogenous RNA (ceRNA) networks are crucial for understanding noncoding RNA roles in complex diseases.
Traditional graph models struggle with long-range dependencies and noise in biological networks.
Existing hypergraph models have limitations in handling graph-level and node-level noise.

Purpose of the Study:

To propose a Noise-Consistent hypeRgraph AutoEncoder (NCRAE) framework for robust node embeddings in ceRNA networks.
To enable precise prediction of cancer-related ceRNA biomarkers.
To improve predictive performance in the presence of noise.

Main Methods:

NCRAE utilizes a multiview contrastive learning strategy with graph-level and node-level corruption.
A noise consistency loss constraint is incorporated to mitigate contrastive learning biases and enhance noise resistance.
Hypergraph convolution and Fourier KAN techniques are employed for effective node embedding learning.

Main Results:

NCRAE demonstrates superior performance compared to existing methods, particularly under noisy conditions.
The framework achieves robust node embedding learning in ceRNA regulatory networks.
Experimental results validate NCRAE's robustness and predictive capability for cancer biomarker discovery.

Conclusions:

NCRAE offers a powerful tool for identifying cancer-related ceRNA biomarkers.
The proposed method effectively addresses noise challenges in ceRNA network analysis.
NCRAE shows significant practical value in cancer biomarker prediction and discovery.