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Researchers introduce intervention-aware distillation framework that uses gene expression data to improve drug discovery image analysis
arXiv cs.CV · 2026年4月28日
AI要約
- •A transcriptome-conditioned teacher model integrates gene expression and intervention metadata to guide image representation learning from microscopy, while an image-only student learns to predict these distributions independently at test time.
- •The framework explicitly handles dose and cell-type mismatches in weakly paired data by employing a fine-tuned single-cell foundation model to encode cell-type context and disentangle dose effects, organizing representations by drug similarity in a chemistry-aware codebook.
- •On Cell Painting and RxRx datasets paired with L1000 transcriptomics, the method significantly improves one-shot transfer to unseen interventions and drug-target gene discovery compared to self-supervised and alignment baselines.
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