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Toward a general framework for AI-enabled prediction in crop improvement.

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Artificial intelligence (AI) and ensembled prediction offer a new framework for crop improvement. This approach enhances predictive accuracy by integrating biological knowledge and computational methods, accelerating genetic gain for complex traits.

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Area of Science:

  • Agricultural Science
  • Computational Biology
  • Genetics

Background:

  • Genomic prediction for complex traits faces challenges due to the curse of dimensionality.
  • Existing methods struggle to effectively utilize complex genetic and physiological network information.

Purpose of the Study:

  • Introduce a theoretical framework for Artificial Intelligence (AI)-enabled prediction in crop improvement.
  • Demonstrate the framework's capabilities and limitations using a logistic map model.

Main Methods:

  • Integrated dynamical systems modeling, ensemble methods, Bayesian statistics, and optimization.
  • Utilized a logistic map to simulate system complexity and assess predictability.
  • Compared prediction of system states versus system process rates.

Main Results:

  • Predictive skill increases with system complexity when using symbolic/sub-symbolic AI.
  • Heritability and predictability decrease with increasing system complexity, aligning with empirical data.
  • Predicting system process rates is more effective than predicting system states for complex systems.

Conclusions:

  • AI-enabled prediction frameworks can overcome the curse of dimensionality in genomic prediction.
  • Leveraging prior biological knowledge and computational approaches enhances prediction accuracy.
  • This integrated approach promises to accelerate genetic gain in crop breeding programs.