Advancing plant single-cell genomics with foundation models.

Abstract

Single-cell genomics, combined with advanced AI models, hold transformative potential for understanding complex biological processes in plants. This article reviews deep-learning approaches in single-cell genomics, focusing on foundation models, a type of large-scale, pretrained, multi-purpose generative AI models. We explore how these models, such as Generative Pre-trained Transformers (GPT), Bidirectional Encoder Representations from Transformers (BERT), and other Transformer-based architectures, are applied to extract meaningful biological insights from diverse single-cell datasets. These models address challenges in plant single-cell genomics, including improved cell-type annotation, gene network modeling, and multi-omics integration. Moreover, we assess the use of Generative Adversarial Networks (GANs) and diffusion models, focusing on their capacity to generate high-fidelity synthetic single-cell data, mitigate dropout events, and handle data sparsity and imbalance. Together, these AI-driven approaches hold immense potential to enhance research in plant genomics, facilitating discoveries in crop resilience, productivity, and stress adaptation.