PhenoProfiler: advancing phenotypic learning for image-based drug discovery.

ABSTRACT: In image-based drug discovery, accurately capturing cellular phenotypic responses to chemical perturbations is crucial for understanding drug mechanisms and predicting efficacy. However, existing approaches often depend on complex, multi-step pipelines that are computationally intensive and prone to error. PhenoProfiler addresses these challenges with an efficient, end-to-end deep learning framework that directly transforms high-content, multi-channel cellular images into low-dimensional quantitative representations. Evaluated on nearly 400,000 high-content images and 8.42 million single-cell images, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20% in both accuracy and robustness. Its tailored phenotype correction strategy further emphasizes treatment-induced va

SUBMITTER: Li B 

PROVIDER: S-EPMC12824340 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature