Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.
Yashi, Kumar N, Kumar R, Singh RK
Crop Improvement
The tomatoes, wheat, and corn being bred right now using these AI tools are the ones that will need to survive the hotter, drier summers your garden already feels — this research is how breeders stay ahead of the climate curve.
Plants produce enormous amounts of biological information — from their DNA to the proteins they make to how they respond to heat or drought. Until now, scientists struggled to make sense of all this data at once because it's so complex and messy. This review describes how AI tools are being trained to connect all these layers of information, helping researchers predict which plants will thrive under stress and breed more resilient crops faster than ever before.
Key Findings
AI methods including deep learning, graph neural networks, and generative models can integrate genomic, proteomic, and metabolomic data to uncover molecular networks driving crop resilience and growth.
Key technical barriers — including incomplete datasets, high data dimensionality, and cross-species inconsistency — are being addressed through AI techniques like missing-value imputation, dimensionality reduction, and transfer learning.
Emerging approaches such as single-cell omics, spatial transcriptomics, and explainable AI frameworks are highlighted as the next frontier for making multi-omics insights interpretable and accessible to a broader research community.
chevron_right Technical Summary
Scientists are using artificial intelligence to combine multiple types of plant biological data — genes, proteins, and metabolism — into unified models that can predict how crops will grow and respond to stress, moving plant science from observation toward actionable forecasting.
Abstract Preview
The convergence of artificial intelligence (AI) and multi-omics is redefining plant science. It moves plant biology from descriptive to predictive and systems-level understanding. Multi-omics frame...
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