Marvin Sextro

[1/3] Happy to share that our paper "MapPFN: Learning Causal Perturbation Maps in Context" has been accepted at NeurIPS 2026! 🎉 TL;DR: We frame perturbation prediction as an in-context learning problem given a set of interventional experiments.

[2/3] MapPFN brings prior-data fitted networks to perturbation prediction, adapting to new biological contexts and gene sets. 🌐 Website: marvinsxtr.github.io/MapPFN 📄 Paper: arxiv.org/abs/2601.21092 💻 Code: github.com/marvinsxtr/MapPFN

[1/6] How can we build virtual cell foundation models that adapt to unseen biological contexts? Meet MapPFN, the first prior-data fitted network (PFN) for perturbation prediction. Meta-learned from a synthetic biological prior, it adapts at inference via in-context learning. 🧵

[5/6] 🌐 Project Page: marvinsxtr.github.io/MapPFN/ 📄 Paper: arxiv.org/pdf/2601.21092 💻 Code: github.com/marvinsxtr/M... Huge thanks to Weronika Kłos and Gabriel Dernbach for this collaboration, and to @tuberlin.bsky.social, @bifold.berlin , Aignostics, and Charité for their support!

[1/6] How can we build virtual cell foundation models that adapt to unseen biological contexts? Meet MapPFN, the first prior-data fitted network (PFN) for perturbation prediction. Meta-learned from a synthetic biological prior, it adapts at inference via in-context learning. 🧵

[3/6] MapPFN meta-learns to map pre- to post-perturbation distributions from a synthetic biological prior of in silico gene knockouts, decoupling it from limited experimental data. At inference, it adapts to unseen biological contexts via in-context learning.

[1/6] How can we build virtual cell foundation models that adapt to unseen biological contexts? Meet MapPFN, the first prior-data fitted network (PFN) for perturbation prediction. Meta-learned from a synthetic biological prior, it adapts at inference via in-context learning. 🧵