Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future...

Area of Science:

• Plant Biology
• Computational Biology
• Genomics

Background:

• Multi-omics data integration is crucial for understanding plant growth, stress adaptation, and crop resilience.
• Challenges include data heterogeneity, high dimensionality, and missing values, hindering interpretation.
• Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for these complex datasets.

Purpose of the Study:

• To review advances in AI-enabled multi-omics integration for plant enhancement.
• To explore AI methodologies for bridging gaps in multi-omics data analysis.
• To highlight AI's role in improving interpretability, scalability, and cross-species transferability in crop research.

Main Methods:

• Review of AI and ML methodologies applied to multi-omics data.
• Analysis of traditional statistical models, deep learning architectures (CNNs, RNNs, GNNs), autoencoders, and GANs.
• Examination of AI applications in dimensionality reduction, missing-value imputation, and network prediction.

Main Results:

• AI and ML effectively model complex molecular networks driving plant traits.
• AI enhances the interpretability and scalability of multi-omics data analysis.
• Emerging techniques like single-cell, spatial, and explainable AI frameworks show promise.

Conclusions:

• AI-driven multi-omics integration is key to developing predictive plant science.
• Standardized, accessible, and interpretable pipelines are needed to democratize multi-omics data use.
• This integration promises resilient, high-yielding crop systems to meet global sustainability goals.