基于影像表型和扰动转录组的干预感知多尺度表征学习
Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics
AI总结：
本文提出一种干预感知蒸馏框架，利用扰动转录组指导图像表征学习，通过基因表达和干预元数据生成化学感知的代码本，提升对未见干预的迁移能力及药物-靶点基因发现。
摘要：
基于显微镜的表型谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱谱 Spectrum of the problem: The paper introduces 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 codebook organized by drug similarity. The teacher employs a fine-tuned single-cell foundation model to encode cell-type context and disentangle dose effects. An image-only student learns to predict these distributions from microscopy alone, distilling mechanistic knowledge while operating independently at test time. This design emphasizes intervention semantics rather than identity alignment and explicitly handles dose and cell-type mismatches. We provide theoretical guarantees showing that transcriptomic guidance tightens the risk bound for image-based prediction. On Cell Painting and RxRx datasets paired with L1000, our method significantly improves one-shot transfer to unseen interventions and drug-target gene discovery compared to self-supervised and alignment baselines.
英文摘要：
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 codebook organized by drug similarity. The teacher employs a fine-tuned single-cell foundation model to encode cell-type context and disentangle dose effects. An image-only student learns to predict these distributions from microscopy alone, distilling mechanistic knowledge while operating independently at test time. This design emphasizes intervention semantics rather than identity alignment and explicitly handles dose and cell-type mismatches. We provide theoretical guarantees showing that transcriptomic guidance tightens the risk bound for image-based prediction. On Cell Painting and RxRx datasets paired with L1000, our method significantly improves one-shot transfer to unseen interventions and drug-target gene discovery compared to self-supervised and alignment baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
