arXiv:2605.30810v1 Announce Type: new Abstract: High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SNE and UMAP, cannot incorporate time-ordering in their layouts, obfuscating the dynamics of cell types or other classes. As a solution, we present IRIS, a new Manifold Learning algorithm that structures layouts both chronologically and by manifold topology. IRIS can visualize a wide range of dynamic biomedical data, including scRNA-seq, comparative metagenomics, and literature.
The increasing generation of complex, high-dimensional biomedical datasets necessitates advanced tools for temporal organization and visualization to extract meaningful dynamic insights.
IRIS addresses a critical limitation in current manifold learning techniques by inherently incorporating temporal ordering, offering superior dynamic visualization for complex biological and other time-series data, thereby accelerating research and discovery.
Scientists will now have a more powerful and intuitive method to visualize time-ordered changes within high-dimensional data, leading to a deeper understanding of cellular dynamics, disease progression, and other complex processes.
- · Biomedical researchers
- · Pharmaceutical industry
- · Data scientists
- · AI/ML algorithm developers
- · Legacy manifold learning algorithms (t-SNE, UMAP, without temporal integration)
- · Companies relying solely on static data visualization tools
Improved visualization and interpretability of complex temporal biomedical data.
Faster discovery of disease biomarkers, therapeutic targets, and fundamental biological mechanisms due to enhanced pattern recognition.
New classes of AI models that can better leverage and learn from time-structured manifold data, creating more accurate predictive and diagnostic tools.
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Read at arXiv cs.LG