PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling | Paper Citation Record

Observation a51419b1-ac67-4028-9d2d-61ce283a2c25 · outbound

This paper cites Computational approaches streamlining drug discovery.Nature, 616(7958):673–685, 2023.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Computational approaches streamlining drug discovery.Nature, 616(7958):673–685, 2023

Reference 1

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Observation 7e36839f-04d5-4ea5-9999-9b1b672453b8 · outbound

This paper cites Predicting causal effects in large-scale systems from observational data.Nature methods, 7(4):247–248, 2010.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Predicting causal effects in large-scale systems from observational data.Nature methods, 7(4):247–248, 2010

Reference 2

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Observation 3bb25a04-b506-4add-b051-1c6cbfbcf9fa · outbound

This paper cites Joint causal inference from multiple contexts.Journal of machine learning research, 21(99):1–108, 2020.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Joint causal inference from multiple contexts.Journal of machine learning research, 21(99):1–108, 2020

Reference 3

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Observation 69fde380-7957-4c00-b467-4c6c073144f6 · outbound

This paper cites Gen- erative intervention models for causal perturbation modeling.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Gen- erative intervention models for causal perturbation modeling

Reference 4

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Observation 845bed32-b14d-4dbc-ae3d-a9992bd6888f · outbound

This paper cites Causal machine learning for single-cell genomics.Nature Genetics, 57(4): 797–808, 2025.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Causal machine learning for single-cell genomics.Nature Genetics, 57(4): 797–808, 2025

Reference 5

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Observation 302f1b4f-5d66-42fc-98a4-33cd381be0f2 · outbound

This paper cites Predicting cellular responses to novel drug perturbations at a single-cell resolution.Advances in Neural Information Processing Systems, 35:26711–26722, 2022.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Predicting cellular responses to novel drug perturbations at a single-cell resolution.Advances in Neural Information Processing Systems, 35:26711–26722, 2022

Reference 6

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Observation 993c4570-85a8-4da4-a1a4-63dec1bf3e5e · outbound

This paper cites Learning single- cell perturbation responses using neural optimal transport.Nature methods, 20(11):1759–1768, 2023.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Learning single- cell perturbation responses using neural optimal transport.Nature methods, 20(11):1759–1768, 2023

Reference 7

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Observation ffb0a439-642c-49a7-b802-fb22e27eda29 · outbound

This paper cites Predicting cellular responses to complex perturbations in high-throughput screens.Molecular systems biology, 19(6):MSB202211517, 2023.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Predicting cellular responses to complex perturbations in high-throughput screens.Molecular systems biology, 19(6):MSB202211517, 2023

Reference 8

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Observation 7b77ffbe-39f8-4ae8-bbda-eafe4b373857 · outbound

This paper cites Learning independent causal mechanisms.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Learning independent causal mechanisms

Reference 9

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Observation a6c14d7d-b055-46d4-a3b4-ebbf663c1c23 · outbound

This paper cites Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature Biomedical Engineering, pages 1–18, 2025.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature Biomedical Engineering, pages 1–18, 2025

Reference 10

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Observation 7fe41b7e-9b6a-47ea-bc2a-391e38034e27 · outbound

This paper cites Predicting transcriptional outcomes of novel multigene perturbations with gears.Nature Biotechnology, 42(6):927–935, 2024.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Predicting transcriptional outcomes of novel multigene perturbations with gears.Nature Biotechnology, 42(6):927–935, 2024

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Observation 007f7a38-7738-4b0a-8cb1-31769172bcfc · outbound

This paper cites scgpt: Towards building a foundation model for single-cell multi-omics using generative ai.bioRxiv, 2023.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling scgpt: Towards building a foundation model for single-cell multi-omics using generative ai.bioRxiv, 2023

Reference 12

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Observation cd8765a4-3a7d-440c-946a-c560c6dfcb55 · outbound

This paper cites Chen and James Zou.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Chen and James Zou

Reference 13

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Observation ee178105-c6c5-4161-aaf1-cf60f3f28911 · outbound

This paper cites In-silico biological discovery with large perturbation models.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling In-silico biological discovery with large perturbation models

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Observation c41ef31e-cb5b-44a6-b64c-f6eb897d3334 · outbound

This paper cites Transformers can do bayesian inference.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Transformers can do bayesian inference

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Observation 2cc30128-417e-4bd4-ae3d-7bddefc92ff7 · outbound

This paper cites Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326, 2025.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326, 2025

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Observation 99ff98a5-4f87-47c2-aa97-3dec6601a89f · outbound

This paper cites Do-pfn: In-context learning for causal effect estimation.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Do-pfn: In-context learning for causal effect estimation

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Observation eefe0fd5-567c-44b4-8aad-6f07d166054d · outbound

This paper cites Tabicl: A tabular foundation model for in-context learning on large data.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Tabicl: A tabular foundation model for in-context learning on large data

Reference 18

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Observation f86cc39c-4e12-46fa-914d-e252f795e08c · outbound

This paper cites TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models

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Observation 24a9655a-f908-4f7b-8a8b-f93d7077887e · outbound

This paper cites Massively multiplex chemical transcriptomics at single-cell resolution.Science, 367(6473): 45–51, 2020.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Massively multiplex chemical transcriptomics at single-cell resolution.Science, 367(6473): 45–51, 2020

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Observation cdf81921-d504-4673-9b2a-dd2071afd017 · outbound

This paper cites genernib: a living benchmark for gene regulatory network inference.bioRxiv, pages 2025–02, 2025.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling genernib: a living benchmark for gene regulatory network inference.bioRxiv, pages 2025–02, 2025

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Observation 068efeb0-22b6-4e4e-9f0f-cf54a683360c · outbound

This paper cites Gene regulatory network structure informs the distribution of perturbation effects.PLOS Computational Biology, 21(9):e1013387, 2025.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Gene regulatory network structure informs the distribution of perturbation effects.PLOS Computational Biology, 21(9):e1013387, 2025

Reference 22

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Observation 02449ca2-10c5-4b3a-8fb2-08f9b9cf09cb · outbound

This paper cites Bartlett, Melodie Li, Chenyu Song, Yuche Gao, and Qiulin Huang.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Bartlett, Melodie Li, Chenyu Song, Yuche Gao, and Qiulin Huang

Reference 23

Resolution: malformed identifier; no resolver link, observed 2026-07-30T21:57:08.283154Z

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Observation 3e6dafb9-ee9e-45bb-aa30-185c96ee2964 · outbound

This paper cites Sergio: a single-cell expression simulator guided by gene regulatory networks.Cell systems, 11(3):252–271, 2020.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Sergio: a single-cell expression simulator guided by gene regulatory networks.Cell systems, 11(3):252–271, 2020

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Observation 232136f1-c742-4e68-8efb-39f02ad37b4f · outbound

This paper cites MapPFN: Learning Causal Perturbation Maps in Context.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling MapPFN: Learning Causal Perturbation Maps in Context

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Observation 188ff66e-76fc-4c0d-9568-027cd571c9fe · outbound

This paper cites A meta-learning approach to bayesian causal discovery.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling A meta-learning approach to bayesian causal discovery

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Observation 18e5a73b-48e9-4d2b-bd4f-9343b16eda0c · outbound

This paper cites Dags with no tears: Continuous optimization for structure learning.Advances in neural information processing systems, 31, 2018.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Dags with no tears: Continuous optimization for structure learning.Advances in neural information processing systems, 31, 2018

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Observation e37931bf-4e36-48ad-acd4-f890bb2ce14a · outbound

This paper cites scgen predicts single-cell perturbation responses.Nature methods, 16(8):715–721, 2019.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling scgen predicts single-cell perturbation responses.Nature methods, 16(8):715–721, 2019

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Observation 2f1d48e9-6aa5-481e-9538-b60f5d0a6430 · outbound

This paper cites Disentanglement of single-cell data with biolord.Nature Biotechnology, 42(11):1678–1683, 2024.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Disentanglement of single-cell data with biolord.Nature Biotechnology, 42(11):1678–1683, 2024

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This paper cites Modelling cellular perturbations with the sparse additive mechanism shift variational autoencoder.Advances in Neural Information Processing Systems, 36:1–12, 2023.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Modelling cellular perturbations with the sparse additive mechanism shift variational autoencoder.Advances in Neural Information Processing Systems, 36:1–12, 2023

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This paper cites Supervised training of conditional monge maps.Advances in Neural Information Processing Systems, 35:6859–6872, 2022.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Supervised training of conditional monge maps.Advances in Neural Information Processing Systems, 35:6859–6872, 2022

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This paper cites Permutation-based causal structure learning with unknown intervention targets.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Permutation-based causal structure learning with unknown intervention targets

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Observation 5018c617-e75b-472e-a153-3ab8cd295601 · outbound

This paper cites Bacadi: Bayesian causal discovery with unknown interventions.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Bacadi: Bayesian causal discovery with unknown interventions

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This paper cites Learning genetic perturbation effects with varia- tional causal inference.PLOS Computational Biology, 22(2):e1013194, 2026.

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Tabiclv2: A better, faster, scalable, and open tabular foundation model.arXiv preprint arXiv:2602.11139, 2026

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This paper cites Causalpfn: Amortized causal effect estimation via in-context learning.arXiv preprint arXiv:2506.07918, 2025.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Causalpfn: Amortized causal effect estimation via in-context learning.arXiv preprint arXiv:2506.07918, 2025

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Observation 31322e58-e60e-4a5b-b52f-8de2c4339807 · outbound

This paper cites Turner, and Mark van der Wilk.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Turner, and Mark van der Wilk

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Foundation models for causal inference via prior-data fitted networks.arXiv preprint arXiv:2506.10914, 2025

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Transfer learning enables predictions in network biology.Nature, 618(7965):616–624, 2023

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Stack: In-context learning of single-cell biology.bioRxiv, 2026

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Metalearners for estimating heterogeneous treatment effects using machine learning.Proceedings of the national academy of sciences, 116(10):4156–4165, 2019

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling ppcor: an r package for a fast calculation to semi-partial correlation coefficients

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Grnboost2 and arboreto: efficient and scalable inference of gene regulatory networks.Bioinformatics, 35(12):2159–2161, 2019

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.Bioinformatics, 38(10): 2802–2809, 2022

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Scenic: single-cell regulatory network inference and clustering.Nature methods, 14(11):1083–1086, 2017

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Causal de finetti: On the identification of invariant causal structure in exchangeable data.Advances in Neural Information Processing Systems, 36:36463–36475, 2023

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Observation 9f48fd72-e809-4c06-848e-8231b09483d3 · outbound

This paper cites MIT press, 2000.

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling MIT press, 2000

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Observation 077c92a5-9259-488b-938b-ca143995931d · outbound

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Emergence of scaling in random networks.science, 286(5439):509–512, 1999

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PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling Position: The future of bayesian prediction is prior-fitted

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