arXiv:2605.24162v1 Announce Type: new Abstract: Biological systems are governed by structured molecular interactions, where pathways, regulatory circuits, and functional gene relationships shape cellular behavior and disease progression. Much of this knowledge is naturally represented as graphs. However, most biomedical AI models cannot directly use graph-encoded biological knowledge and instead require compressed low-dimensional representations, which can lose important structure and reduce performance, especially in limited-sample clinical studies. Here, we introduce Graph-in-Graph (GiG), a
This development leverages advancements in deep learning with the increasing availability and sophistication of biological knowledge graphs, addressing the critical challenge of limited clinical data.
It improves the ability of AI models to derive insights from complex biological data, potentially accelerating drug discovery, personalized medicine, and clinical decision support, especially where data is scarce.
AI models can now more effectively integrate structured biological knowledge directly, moving beyond compressed representations to retain crucial information from biological systems.
- · Biomedical AI developers
- · Pharmaceutical industry
- · Healthcare providers
- · Biotech startups
- · Traditional statistical modeling approaches for biological data
- · AI models reliant solely on raw, unstructured clinical data
Improved accuracy and robustness of AI models in clinical diagnostics and prognostics.
Faster development cycles for new therapies and more targeted interventions for complex diseases.
The creation of a new generation of 'knowledge-aware' AI systems that bridge mechanistic understanding with data-driven predictions in biology and beyond.
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Read at arXiv cs.LG