Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics
Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scarce. Existing multimodal approaches either use images to support other modalities or naively align representations by sample identity, ignoring cell-type and dose variations in weakly paired data-limiting generalization to unseen interventions. In this paper, we introduce an intervention-aware distillation framework that leverages perturbational transcriptomics to guide image representation learning. A transcriptome-conditioned teacher integrates gene expression and intervention metadata to produce soft distributions over a chemistry-aware co...
| Authors | Jiayuan Chen, Ruoqi Liu, Zishan Gu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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