Identification, characterization, and design of plant genome sequences using deep learning

Area of Science:

• Plant Biology
• Bioinformatics
• Genomics

Background:

• Deep learning excels at processing large datasets and modeling complex relationships.
• Its application in plant biology is rapidly expanding across various research areas.

Purpose of the Study:

• To review the current applications of deep learning in plant biology.
• To discuss advancements in motif mining, functional component design, and protein/genomic prediction.
• To provide future prospects for deep learning in plant science.

Main Methods:

• Review of deep learning techniques applied to plant genome sequence analysis.
• Elaboration on generative adversarial networks, large models, and attention mechanisms for motif mining and design.
• Discussion of deep learning for protein structure/function prediction and genomic prediction.

Main Results:

• Deep learning accurately predicts gene expression, chromatin interactions, and epigenetic features from plant genome sequences.
• Current methods enable motif mining and functional component design using advanced AI.
• Progress is evident in protein structure/function and genomic prediction.

Conclusions:

• Deep learning is a powerful tool for plant biology research, offering insights into genomics and epigenetics.
• Advancements in AI models are driving innovation in sequence design and functional prediction.
• Future directions include integrating multi-omics data and developing large language models for intelligent plant breeding.