arXiv Machine Learning
Aug 24

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

PerturbRx is a treatment‑conditioned representation learning framework that learns latent transitions induced by drug interventions. It trains a drug‑ and dose‑conditioned transition predictor using control and treated single‑cell populations, then applies this predictor to pretreatment patient profiles to generate response features without needing post‑treatment data. On TCGA and patient‑derived xenograft benchmarks, PerturbRx outperforms other methods, demonstrating the value of perturbation‑pretrained latent transitions for patient‑level drug‑response prediction.

By Yoshitaka Inoue, Minoh Jeong, Alfred Hero, Rui Kuang, Augustin Luna
arXiv Machine Learning
Oct 9

Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

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.

By Andrea Giuseppe Di Francesco, Andrea Rubbi, Rishabh Jain, Pietro Li\`o
arXiv Machine Learning
4d ago

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