GRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana
arXiv:2606.27413v1 Announce Type: cross Abstract: Understanding which genes control which traits in an organism remains one of the central challenges in biology. Despite significant advances in data collection technology, our ability to map genes to traits is still limited. This genome-to-phenome (G2P) challenge spans several problem domains, including plant breeding, and requires methods capable of reasoning over high-dimensional, heterogeneous, and biologically structured data. Current datasets and data repositories, however, are not well-equipped for this task. Current studies do not link g
The increasing availability of high-dimensional biological data and advances in graph machine learning are converging to enable new methods for understanding complex biological systems.
This research provides critical tools for accelerating the 'genome-to-phenome' challenge, which is fundamental to breakthroughs in plant breeding and synthetic biology, impacting global food security and bio-innovation.
The development of novel graph and hypergraph benchmarks for gene-trait prediction offers enhanced capabilities for reasoning over complex biological data, potentially leading to more efficient and accurate biological engineering.
- · Synthetic Biology Researchers
- · Agricultural Biotechnology
- · AI/ML for Drug Discovery
- · Plant Breeders
- · Traditional Genetics Research
Improved prediction of gene function and trait expression in organisms like Arabidopsis thaliana.
Faster development of new crop varieties with enhanced characteristics, such as disease resistance or higher yields.
Potentially a broader application of these AI methods to human genomics and personalized medicine, leading to more targeted therapies.
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Read at arXiv cs.AI