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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

CSGDN: contrastive signed graph diffusion network for predicting crop gene-phenotype associations.

Yiru Pan¹, Xingyu Ji¹, Jiaqi You¹

¹National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, 430070 Hubei, China.

Briefings in Bioinformatics

|February 20, 2025

Summary

A new Contrastive Signed Graph Diffusion Network (CSGDN) model improves gene-phenotype association prediction accuracy. This method reduces the need for large sample sizes and minimizes experimental noise for robust biological insights.

Keywords:

gene–phenotype associations graph neural networks signed bipartite networks signed graph neural networks

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Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Predicting gene-phenotype associations is crucial for understanding complex traits.
Current methods face challenges with high costs, large sample requirements, and experimental/computational noise.

Purpose of the Study:

To develop a robust model for accurate gene-phenotype association prediction.
To address limitations of sample size and noise in existing methods.

Main Methods:

Proposed a Contrastive Signed Graph Diffusion Network (CSGDN).
Employed signed graph diffusion to identify regulatory associations.
Utilized stochastic perturbation for multi-view learning and contrastive loss to reduce noise.

Main Results:

CSGDN achieved higher link prediction accuracy with fewer training samples.
Demonstrated superior performance over state-of-the-art methods on crop datasets (Gossypium hirsutum, Brassica napus, Triticum turgidum).
Achieved up to 9.28% higher AUC for link sign prediction in Gossypium hirsutum.

Conclusions:

CSGDN offers a more efficient and accurate approach for gene-phenotype association prediction.
The model's robustness to noise enhances its applicability in biological research.
The developed method has implications for understanding genetic regulation in crops.