When models choose metrics: Hidden geometry in computational biology.

Careful probabilistic modeling reveals that Fisher information naturally defines local metrics, shaping biological data analysis and clarifying metric choices in computational biology.

• Why it matters: Choosing the right distance metric influences the biological questions addressed and can introduce biases based on assumptions about noise and similarity; understanding this helps improve interpretability and accuracy.
• What they did: The authors examine how probabilistic models, especially regular parametric ones, support local metric structures via Fisher information, and how this guides the use of transformations like variance stabilization in RNA-seq data.
• The result: This approach clarifies when common transformations approximate model-implied distances, highlights limitations due to dependence or latent structures, and advocates for explicit metric selection to enhance analysis robustness.