IRIS reconstructs cell-signaling histories across cell types using perturbation screens and transfer learning

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Summary

IRIS 是一种利用扰动筛选与迁移学习重建跨细胞类型细胞信号传导历史的新方法,发表于 Nature Methods。

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MIT researchers led by Pulin Li developed IRIS, a neural network model that detects unique gene-activity 'fingerprints' left by cellular signaling pathways, allowing them to reconstruct a cell's signaling history across many cell types using transfer learning. Published in Nature Methods, the approach could accelerate stem cell engineering, organoid development, and disease research.