Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future...

Area of Science:

• Animal genomics
• Artificial intelligence
• Intelligent agriculture

Background:

• Generative AI represents a paradigm shift from predictive to generative modeling in life sciences.
• Animal genomics is crucial for advancing intelligent agriculture through data integration and predictive modeling.
• Understanding generative model architectures and livestock genomic data is essential for AI applications.

Purpose of the Study:

• To review recent developments, applications, and challenges of generative AI in animal genomics.
• To explore the implications of generative AI for data synthesis, biological sequence design, and smart livestock systems.
• To bridge the gap between computational innovations and biological constraints in livestock genomics.

Main Methods:

• Comprehensive literature review of generative AI applications in animal genomics.
• Analysis of generative model architectures and their relevance to biological sequence representation.
• Examination of livestock genomic and multi-omics data peculiarities.

Main Results:

• Generative AI enables applications like genomic data augmentation, variant prediction, and protein/gene sequence design.
• It enhances genomic selection, trait prediction, and regulatory/epigenomic modeling.
• AI integration facilitates precision livestock farming, digital twins, and sustainability-focused decision-making.

Conclusions:

• Generative AI is a foundational technology for next-generation animal genomics and smart agriculture.
• Interdisciplinary collaboration, rigorous validation, and ethical considerations are vital for responsible AI deployment.
• Future opportunities include generative genome engineering, multimodal modeling, and species preservation.