Summary of Csgdn: Contrastive Signed Graph Diffusion Network For Predicting Crop Gene-phenotype Associations, by Yiru Pan et al.
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
by Yiru Pan, Xingyu Ji, Jiaqi You, Lu Li, Zhenping Liu, Xianlong Zhang, Zeyu Zhang, Maojun Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Contrastive Signed Graph Diffusion Network (CSGDN) tackles two key challenges in predicting positive and negative associations between genes and traits: high-throughput sequencing and phenotyping are costly and time-consuming, while experiments introduce errors and calculations produce noise. CSGDN learns robust node representations with fewer training samples to achieve higher link prediction accuracy. It employs a signed graph diffusion method to uncover regulatory associations and then uses stochastic perturbation strategies to create two views for original and diffusive graphs. A multi-view contrastive learning paradigm loss unifies node presentations learned from the two views, resisting interference and reducing noise. Experimental results on three crop datasets demonstrate that CSGDN outperforms state-of-the-art methods by up to 9.28% AUC for link sign prediction in the Gossypium hirsutum dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand how genes are connected to traits in organisms. This helps us learn about complex traits and how they change over time or in different environments. The current methods for finding these connections have some big problems, like being too expensive and taking too long, and also introducing errors that make it hard to get accurate results. To fix this, the researchers created a new model called CSGDN, which uses a combination of techniques to learn about gene-trait connections with fewer data points and reduced noise. They tested their model on three different crop species and found that it performed better than existing methods. |
Keywords
» Artificial intelligence » Auc » Diffusion
