Article

Analytical perturbation reveals hidden instability of biological phenotypes

2026-07-16

Abstract excerpt

<h4>Background</h4> Unsupervised machine learning has become a cornerstone of computational phenotyping across clinical medicine, genomics, imaging, and multi-omics research. However, phenotype discovery relies on a sequence of analytical decisions – including missing-data handling, preprocessing, dimensionality reduction, clustering methodology, and stochastic initialization – that are rarely evaluated collectiv...

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Literature Corpus work: 62bbe265-c413-50f6-8de2-cc9fd005f928
DOI: 10.64898/2026.07.13.26357916

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