{"ID":2895031,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10737","arxiv_id":"2507.10737","title":"Integrating Biological Knowledge for Robust Microscopy Image Profiling on De Novo Cell Lines","abstract":"High-throughput screening techniques, such as microscopy imaging of cellular responses to genetic and chemical perturbations, play a crucial role in drug discovery and biomedical research. However, robust perturbation screening for \\textit{de novo} cell lines remains challenging due to the significant morphological and biological heterogeneity across cell lines. To address this, we propose a novel framework that integrates external biological knowledge into existing pretraining strategies to enhance microscopy image profiling models. Our approach explicitly disentangles perturbation-specific and cell line-specific representations using external biological information. Specifically, we construct a knowledge graph leveraging protein interaction data from STRING and Hetionet databases to guide models toward perturbation-specific features during pretraining. Additionally, we incorporate transcriptomic features from single-cell foundation models to capture cell line-specific representations. By learning these disentangled features, our method improves the generalization of imaging models to \\textit{de novo} cell lines. We evaluate our framework on the RxRx database through one-shot fine-tuning on an RxRx1 cell line and few-shot fine-tuning on cell lines from the RxRx19a dataset. Experimental results demonstrate that our method enhances microscopy image profiling for \\textit{de novo} cell lines, highlighting its effectiveness in real-world phenotype-based drug discovery applications.","short_abstract":"High-throughput screening techniques, such as microscopy imaging of cellular responses to genetic and chemical perturbations, play a crucial role in drug discovery and biomedical research. However, robust perturbation screening for \\textit{de novo} cell lines remains challenging due to the significant morphological and...","url_abs":"https://arxiv.org/abs/2507.10737","url_pdf":"https://arxiv.org/pdf/2507.10737v1","authors":"[\"Jiayuan Chen\",\"Thai-Hoang Pham\",\"Yuanlong Wang\",\"Ping Zhang\"]","published":"2025-07-14T19:01:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}