Advancing plant single-cell genomics with foundation models.

Area of Science:

• Plant genomics
• Computational biology
• Artificial intelligence

Background:

• Single-cell genomics provides high-resolution insights into plant biology.
• Understanding complex plant processes requires advanced analytical tools.
• AI models offer new capabilities for analyzing large-scale single-cell data.

Purpose of the Study:

• To review deep-learning approaches in plant single-cell genomics.
• To highlight the application of foundation models (e.g., GPT, BERT) in this field.
• To assess generative models (GANs, diffusion models) for synthetic data generation and data challenges.

Main Methods:

• Review of deep-learning architectures, including Transformers (GPT, BERT).
• Exploration of Generative Adversarial Networks (GANs) and diffusion models.
• Focus on applications in cell-type annotation, gene network modeling, and multi-omics integration.

Main Results:

• Foundation models improve biological insight extraction from diverse single-cell datasets.
• Generative models effectively create synthetic data, address dropout events, and manage data sparsity.
• AI models show promise in overcoming key challenges in plant single-cell genomics.

Conclusions:

• AI-driven deep learning, particularly foundation and generative models, significantly advances plant single-cell genomics.
• These approaches enhance data analysis, enabling breakthroughs in crop resilience, productivity, and stress adaptation.
• The integration of AI is crucial for future discoveries in plant science.