arXiv Machine Learning July 28, 2026 By Yuche Gao, Jos\'e Miguel Hern\'andez-Lobato, Siyuan Guo

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling

Read the original on arXiv Machine Learning →

arXiv:2607. 23447v1 Announce Type: new Abstract: Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations

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