Generalizable morphological profiling of cells by interpretable unsupervised learning.

ABSTRACT: The intersection of advanced microscopy and machine learning is transforming cell biology into a quantitative, data-driven field. Traditional cell profiling depends on manual feature extraction, which is labor-intensive and prone to bias, while deep learning provides alternatives but faces challenges with interpretability and reliance on labeled data. We present MorphoGenie, an unsupervised deep-learning framework for single-cell morphological profiling. By combining disentangled representation learning with high-fidelity image reconstruction, MorphoGenie creates a compact, interpretable latent space that captures biologically meaningful features without annotation, overcoming the "curse of dimensionality." Unlike previous models, it systematically links latent representations to hierarchi

SUBMITTER: Murthy RS 

PROVIDER: S-EPMC12749886 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature