arXiv:2606.11646v1 Announce Type: new Abstract: Compositional data -- vectors encoding relative proportions -- arise across scientific domains, including ecology, geochemistry, and genomics. The features in these data often come with known hierarchical structure (e.g., taxonomies, phylogenies, ontologies), yet existing methods either ignore this structure, discard the intrinsic Aitchison geometry, are designed for binary trees, or yield incomplete coordinate systems. We describe PolyILR, a canonical orthonormal decomposition of the Aitchison tangent space aligned with any tree topology. Our co
This research addresses fundamental limitations in handling compositional data with hierarchical structures, a recurring challenge in various scientific and AI domains as data complexity grows.
Improved methods for analyzing complex biological and ecological data could accelerate discoveries in fields critical for medicine, environmental science, and potentially synthetic biology applications.
The proposed PolyILR method offers a more accurate and robust way to decompose tree-structured compositional data, potentially leading to better predictive models and scientific insights where such data are prevalent.
- · Biotech researchers
- · Genomics companies
- · AI/ML researchers in bioinformatics
- · Synthetic biology
More accurate analysis of biological and ecological datasets becomes possible, especially those with inherent hierarchical relationships.
Accelerated discovery of biomarkers or environmental indicators due to better interpretation of compositional data.
New AI models that more effectively integrate and learn from complex, hierarchically structured biological data, driving progress in synthetic biology and drug discovery.
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Read at arXiv cs.LG