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arXiv Machine Learning By Andrea Giuseppe Di Francesco, Andrea Rubbi, Rishabh Jain, Pietro Li\`o

Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

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The paper introduces PT‑RAG, a retrieval‑augmented generation framework that enhances deep learning models for predicting cellular transcriptional responses to gene perturbations. PT‑RAG adds a two‑stage retrieval module—semantic retrieval followed by a Gumbel‑Softmax selector—to supply contextually relevant perturbation information, improving predictive accuracy across cell types and perturbations. Experiments on STATE‑style and scGPT backbones show consistent gains, such as a 5.9% reduction in 2‑Wasserstein distance for cross‑cell‑type predictions.

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Differentiable Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

The paper introduces PT‑RAG, a retrieval‑augmented generation framework that enhances cellular perturbation response prediction by incorporating context from related perturbations. PT‑RAG uses a two‑stage retrieval process—semantic retrieval to shortlist candidate perturbations followed by a differentiable Gumbel‑Softmax selector that adapts the selection based on cell state and query perturbation. Experiments on two backbones, a frozen STATE‑style generator and a fully trained scGPT, show that PT‑RAG improves distributional similarity and predictive quality, achieving a 5.9% reduction in 2‑Wasserstein distance on cross‑cell‑type tasks.

By Andrea Giuseppe Di Francesco, Andrea Rubbi, Rishabh Jain, Pietro Li\`o
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