Interfacing AI and Genomics for Complex Trait Prediction in Plants

The field of plant science is witnessing rapid advancements, particularly through the integration of genomics with artificial intelligence (AI) and machine learning (ML). Innovations in sequencing technologies and computational biology have yielded vast genomic and multi-omics datasets spanning diverse plant species, including crops, trees, and medicinal plants. Concurrently, AI and ML represent promising strategies for data integration, pattern recognition, and predictive modeling. This convergence offers a transformative opportunity in plant science, enabling researchers to uncover the molecular foundations of complex traits and drive forward plant and crop enhancement.Complex traits in plants, such as yield, quality, stress tolerance, and environmental adaptation, have intricate genetic architectures, involving numerous genes, genome structural variations, and dynamic environmental interactions. Traditional methods in genetics and genomics have provided substantial insights but often fall short in capturing the full complexity of these traits. AI and ML emerge as powerful allies, providing tools to analyze extensive genomic and phenotypic datasets, thereby improving trait performance prediction and facilitating the discovery of pivotal regulatory genes and networks.This Research Topic aims to aggregate pioneering research that merges genomics, systems biology, and AI methodologies to dissect the genetic underpinnings of complex plant traits. Our goal is to underscore both fundamental discoveries and applied strategies that bridge the gap between genomic variation and practical trait enhancement.To gather further insights in the realm of AI-assisted genomics for plants, we welcome articles addressing, but not limited to, the following themes:• Genome structural variations and their influence on complex traits• Applications of AI/ML in genome-wide association studies (GWAS), QTL mapping, and trait prediction• Multi-omics integration for trait dissection, covering genomics, transcriptomics, metabolomics, and phenomics• Network biology and the regulatory mechanisms behind trait complexity• Comparative genomics of complex traits across diverse plant lineages• AI-driven approaches for functional gene annotation and predictive breeding strategiesWe welcome contributions across different plant systems, from model organisms to crops, forest species, and medicinal plants. Additionally, this Research Topic invites original research articles, reviews, methods, and perspectives. Topic editors: Chao Shen, Jianying Li Source: https://www.frontiersin.org/research-topics/74039/interfacing-ai-and-genomics-for-complex-trait-prediction-in-plants