Benchmarking virtual cell models for in-the-wild perturbation response | Paper Citation Record

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This paper cites Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens.cell, 167(7): 1853–1866.

Benchmarking virtual cell models for in-the-wild perturbation response Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens.cell, 167(7): 1853–1866

Reference 1

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Observation 34b0bd84-359f-4f58-b025-df176315ea3e · outbound

This paper cites A multiplexed single- cell crispr screening platform enables systematic dissection of the unfolded protein response.

Benchmarking virtual cell models for in-the-wild perturbation response A multiplexed single- cell crispr screening platform enables systematic dissection of the unfolded protein response

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.564631Z

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Observation 26b24f1c-cf1e-48cd-a839-d72e489ba4a7 · outbound

This paper cites Pooled crispr screening with single-cell transcriptome readout.Nature methods, 14(3):297–301.

Benchmarking virtual cell models for in-the-wild perturbation response Pooled crispr screening with single-cell transcriptome readout.Nature methods, 14(3):297–301

Reference 3

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Observation a256c030-66f0-4cb7-9f57-48c15f02681f · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response Massively multiplex chemical transcriptomics at single-cell resolution.Science, 367(6473): 45–51

Reference 4

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Observation 58d58495-66c8-4be8-8780-ae1f3d92ff47 · outbound

This paper cites Genome-scale crispr-cas9 knockout screening in human cells.Science, 343(6166):84–87.

Benchmarking virtual cell models for in-the-wild perturbation response Genome-scale crispr-cas9 knockout screening in human cells.Science, 343(6166):84–87

Reference 5

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Observation a7a05456-3b1c-4c95-88d5-9f32d4cfd055 · outbound

This paper cites High-content crispr screening.Nature Reviews Methods Primers, 2(1):8.

Benchmarking virtual cell models for in-the-wild perturbation response High-content crispr screening.Nature Reviews Methods Primers, 2(1):8

Reference 6

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Observation ceb215ac-1778-4b8a-a963-54e311df3d4f · outbound

This paper cites Machine learning and statistical methods for clustering single-cell rna-sequencing data.Briefings in bioinformatics, 21(4):1209–1223.

Benchmarking virtual cell models for in-the-wild perturbation response Machine learning and statistical methods for clustering single-cell rna-sequencing data.Briefings in bioinformatics, 21(4):1209–1223

Reference 7

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Observation e212dd6e-b7da-451c-8633-efb81e592ab1 · outbound

This paper cites Dissecting cell identity via network inference and in silico gene perturbation.Nature, 614(7949):742–751.

Benchmarking virtual cell models for in-the-wild perturbation response Dissecting cell identity via network inference and in silico gene perturbation.Nature, 614(7949):742–751

Reference 8

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Observation 28998c38-bfe3-452e-980c-7e1b7f78d729 · outbound

This paper cites Applications of single-cell rna sequencing in drug discovery and development.Nature Reviews Drug Discovery, 22(6):496–520.

Benchmarking virtual cell models for in-the-wild perturbation response Applications of single-cell rna sequencing in drug discovery and development.Nature Reviews Drug Discovery, 22(6):496–520

Reference 9

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Observation 794644fa-1fb5-4f03-9ced-c85d5bd6fdf7 · outbound

This paper cites The chemical space project.Accounts of Chemical Research, 48(3):722–730.

Benchmarking virtual cell models for in-the-wild perturbation response The chemical space project.Accounts of Chemical Research, 48(3):722–730

Reference 10

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Observation 3684f451-2a57-4cf5-8fd0-1aed5d555ab1 · outbound

This paper cites Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq.

Benchmarking virtual cell models for in-the-wild perturbation response Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq

Reference 11

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Observation 97597a60-a38b-4bbb-9161-29a6f0741771 · outbound

This paper cites A mini-review on perturbation modelling across single-cell omic modalities.Computational and Structural Biotechnology Journal, 23:1886–1896.

Benchmarking virtual cell models for in-the-wild perturbation response A mini-review on perturbation modelling across single-cell omic modalities.Computational and Structural Biotechnology Journal, 23:1886–1896

Reference 12

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Observation c53e103c-f6cc-450d-8ad3-1f9211e16f37 · outbound

This paper cites Toward a foundation model of causal cell and tissue biology with a perturbation cell and tissue atlas.Cell, 187(17):4520–4545.

Benchmarking virtual cell models for in-the-wild perturbation response Toward a foundation model of causal cell and tissue biology with a perturbation cell and tissue atlas.Cell, 187(17):4520–4545

Reference 13

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Observation bada9e2d-dc01-4022-83f9-a2fb08e16b27 · outbound

This paper cites Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature Methods, 23(2):451–464.

Benchmarking virtual cell models for in-the-wild perturbation response Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature Methods, 23(2):451–464

Reference 14

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Observation 6b4dc4e5-f4d9-4629-99ed-d130e7c5f559 · outbound

This paper cites Perturbench: Benchmarking machine learning models for cellular perturbation analysis.arXiv preprint arXiv:2408.10609.

Benchmarking virtual cell models for in-the-wild perturbation response Perturbench: Benchmarking machine learning models for cellular perturbation analysis.arXiv preprint arXiv:2408.10609

Reference 15

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Observation 832587ee-4f16-497e-bd7b-cf246ab67790 · outbound

This paper cites Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.Nature Methods, 22:1657–1661.

Benchmarking virtual cell models for in-the-wild perturbation response Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.Nature Methods, 22:1657–1661

Reference 16

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Observation 7902a129-3bc0-4230-bdd1-fd2d4b6c4d52 · outbound

This paper cites Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.

Benchmarking virtual cell models for in-the-wild perturbation response Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations

Reference 17

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Observation e5b457ff-c084-4c93-b9b7-f28f80ee4996 · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response scgen predicts single-cell pertur- bation responses.Nature methods, 16(8):715–721

Reference 18

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Observation d772491a-decd-45d3-95e9-f40d7d847bea · outbound

This paper cites Deep generative modeling for single-cell transcriptomics.Nature methods, 15(12):1053–1058.

Benchmarking virtual cell models for in-the-wild perturbation response Deep generative modeling for single-cell transcriptomics.Nature methods, 15(12):1053–1058

Reference 19

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Observation 8b7dc846-a14c-4435-a126-66c410a8126f · outbound

This paper cites Condi- tional out-of-distribution generation for unpaired data using transfer vae.Bioinformatics, 36 (Supplement_2):i610–i617.

Benchmarking virtual cell models for in-the-wild perturbation response Condi- tional out-of-distribution generation for unpaired data using transfer vae.Bioinformatics, 36 (Supplement_2):i610–i617

Reference 20

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Observation 739182af-2059-47a9-b931-ee7e6d926996 · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response Predicting cellular responses to complex perturbations in high-throughput screens.Molecular systems biology, 19(6):MSB202211517

Reference 21

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Observation a9949e7e-a132-4b14-842e-3221c7980edb · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response Learning single-cell perturbation responses using neural optimal transport.Nature Methods, 20(11):1759–1768

Reference 22

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Observation fa0d6c7a-480b-4f8d-b6ff-002e880be894 · outbound

This paper cites Cellflow enables generative single-cell phenotype modeling with flow matching.

Benchmarking virtual cell models for in-the-wild perturbation response Cellflow enables generative single-cell phenotype modeling with flow matching

Reference 23

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Observation 4c8c1890-58ec-450c-b077-ee801f10f445 · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response Predicting transcriptional outcomes of novel multigene perturbations with gears.Nature Biotechnology, 42(6):927–935

Reference 24

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Observation d697c71e-c980-4685-97e9-9c56c0d00eef · outbound

This paper cites Squidiff: predicting cellular development and responses to perturbations using a diffusion model.Nature Methods, 23(1):65–77.

Benchmarking virtual cell models for in-the-wild perturbation response Squidiff: predicting cellular development and responses to perturbations using a diffusion model.Nature Methods, 23(1):65–77

Reference 25

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Observation bd082de2-7dd6-4416-8b9d-f8a785c185fe · outbound

This paper cites scgpt: toward building a foundation model for single-cell multi-omics using generative ai.Nature methods, 21(8):1470–1480.

Benchmarking virtual cell models for in-the-wild perturbation response scgpt: toward building a foundation model for single-cell multi-omics using generative ai.Nature methods, 21(8):1470–1480

Reference 26

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Observation d1dd5c93-240e-4c7b-8e77-f5b46a945c61 · outbound

This paper cites Large-scale foundation model on single-cell tran- scriptomics.Nature Methods, 21(8):1481–1491.

Benchmarking virtual cell models for in-the-wild perturbation response Large-scale foundation model on single-cell tran- scriptomics.Nature Methods, 21(8):1481–1491

Reference 27

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Observation f8979093-5bc8-40f3-ae2f-15087a421157 · outbound

This paper cites Transfer learning enables predictions in network biology.Nature, 618(7965):616–624.

Benchmarking virtual cell models for in-the-wild perturbation response Transfer learning enables predictions in network biology.Nature, 618(7965):616–624

Reference 28

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Observation c282184e-d638-4908-abe8-d1cca7031c3b · outbound

This paper cites scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data.Nature Machine Intelligence, 4(10):852–866.

Benchmarking virtual cell models for in-the-wild perturbation response scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data.Nature Machine Intelligence, 4(10):852–866

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.501517Z

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Observation a7f0a8ab-93c2-4f40-9c21-bba27f4882ab · outbound

This paper cites Benchmarking foundation cell models for post-perturbation rna-seq prediction.BMC genomics, 26(1):393.

Benchmarking virtual cell models for in-the-wild perturbation response Benchmarking foundation cell models for post-perturbation rna-seq prediction.BMC genomics, 26(1):393

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Observation 0750f3a4-5c85-4da1-952e-54e754f65ce9 · outbound

This paper cites Perteval-scfm: bench- markingsingle-cellfoundationmodelsforperturbationeffectprediction.BioRxiv, pages2024–10.

Benchmarking virtual cell models for in-the-wild perturbation response Perteval-scfm: bench- markingsingle-cellfoundationmodelsforperturbationeffectprediction.BioRxiv, pages2024–10

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Observation fbe4fee5-f601-404a-9ff7-8fc1847eb1c3 · outbound

This paper cites A systematic comparison of computational methods for expression forecasting.bioRxiv.

Benchmarking virtual cell models for in-the-wild perturbation response A systematic comparison of computational methods for expression forecasting.bioRxiv

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Observation d1b4d2a1-d7e4-422d-90c8-c0b7bfc14da1 · outbound

This paper cites Single-cell perturbation prediction: generalizing experimental interventions to unseen contexts.

Benchmarking virtual cell models for in-the-wild perturbation response Single-cell perturbation prediction: generalizing experimental interventions to unseen contexts

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Observation 00a0890d-8eda-425b-b138-afe9403c55f4 · outbound

This paper cites Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all.

Benchmarking virtual cell models for in-the-wild perturbation response Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all

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Resolution: verified exact; arxiv_id, observed 2026-05-12T10:41:29.570635Z

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Observation b8590b29-5e75-44e4-b8b8-6a7dc216d5c8 · outbound

This paper cites scperturb: harmonizedsingle-cell perturbation data.Nature Methods, 21(3):531–540.

Benchmarking virtual cell models for in-the-wild perturbation response scperturb: harmonizedsingle-cell perturbation data.Nature Methods, 21(3):531–540

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.540805Z

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Observation 8022a2d6-2fde-4e02-a1c2-d1d341b305e8 · outbound

This paper cites Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.Nature communications, 11(1):4296.

Benchmarking virtual cell models for in-the-wild perturbation response Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.Nature communications, 11(1):4296

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Observation 50851bfd-6688-4c71-95e5-23a8e7406240 · outbound

This paper cites Multiplexed droplet single-cell rna-sequencing using natural genetic variation.Nature biotechnology, 36(1): 89–94.

Benchmarking virtual cell models for in-the-wild perturbation response Multiplexed droplet single-cell rna-sequencing using natural genetic variation.Nature biotechnology, 36(1): 89–94

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Observation 2a507706-4e0a-4fd6-bcbe-4f54133247ef · outbound

This paper cites Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.Science, 365(6455):786–793.

Benchmarking virtual cell models for in-the-wild perturbation response Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.Science, 365(6455):786–793

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Observation 6e77cabb-9dc5-47ea-9f07-f2f57e11e7f9 · outbound

This paper cites A kernel two-sample test.Journal of Machine Learning Research, 13(25):723–773.

Benchmarking virtual cell models for in-the-wild perturbation response A kernel two-sample test.Journal of Machine Learning Research, 13(25):723–773

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Observation ec785251-39d5-4b90-822e-fcfe3f381c6a · outbound

This paper cites Energy statistics: A class of statistics based on distances.

Benchmarking virtual cell models for in-the-wild perturbation response Energy statistics: A class of statistics based on distances

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Observation 55064dcf-b69e-4394-a452-6499a6a256c0 · outbound

This paper cites Optimal distance metrics for single-cell rna-seq populations.

Benchmarking virtual cell models for in-the-wild perturbation response Optimal distance metrics for single-cell rna-seq populations

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Observation 674f5607-717a-428b-a09a-e9526db82469 · outbound

This paper cites scpram accurately predicts single- cell gene expression perturbation response based on attention mechanism.Bioinformatics, 40 (5):btae265.

Benchmarking virtual cell models for in-the-wild perturbation response scpram accurately predicts single- cell gene expression perturbation response based on attention mechanism.Bioinformatics, 40 (5):btae265

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.533138Z

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Observation 1a12ef0f-7bde-460b-a97b-4aff85a55cc2 · outbound

This paper cites Benchmarking atlas-level data integration in single-cell genomics.Nature Methods, 19(1):41–50.

Benchmarking virtual cell models for in-the-wild perturbation response Benchmarking atlas-level data integration in single-cell genomics.Nature Methods, 19(1):41–50

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Observation a9f530c2-ce57-495c-9d6d-71bca3f05265 · outbound

This paper cites Scaling Laws for Neural Language Models.

Benchmarking virtual cell models for in-the-wild perturbation response Scaling Laws for Neural Language Models

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Resolution: verified exact; local_arxiv, observed 2026-05-12T10:41:29.564244Z

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Observation e92b4b57-8912-4922-a599-2bcf009e8c77 · 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.

Benchmarking virtual cell models for in-the-wild perturbation response Predicting cellular responses to novel drug perturbations at a single-cell resolution.Advances in Neural Information Processing Systems, 35:26711–26722

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Observation 10ccc375-1948-420c-b59a-1a648319eceb · outbound

This paper cites Comprehensive integra- tion of single-cell data.cell, 177(7):1888–1902.

Benchmarking virtual cell models for in-the-wild perturbation response Comprehensive integra- tion of single-cell data.cell, 177(7):1888–1902

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.573251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6fcece42-cb93-479c-8ed1-8b7600bd5607 · outbound

This paper cites Tahoe-100m: A giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling.bioRxiv.

Benchmarking virtual cell models for in-the-wild perturbation response Tahoe-100m: A giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling.bioRxiv

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.625385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation aa659bc7-2123-46a6-8804-8354f867c9dc · outbound

This paper cites X-atlas/orion: Genome-wide perturb-seq datasets via a scalable fix- cryopreserve platform for training dose-dependent biological foundation models.bioRxiv.

Benchmarking virtual cell models for in-the-wild perturbation response X-atlas/orion: Genome-wide perturb-seq datasets via a scalable fix- cryopreserve platform for training dose-dependent biological foundation models.bioRxiv

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.557602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9b89bca7-8e28-4320-b40e-9a25fbbc5b04 · outbound

This paper cites an unresolved cited work.

Benchmarking virtual cell models for in-the-wild perturbation response Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:53:59.668013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e0780f2-8b25-4ab8-9fab-5536aed3bf96 · outbound

This paper cites Scanpy: large-scale single-cell gene expression data analysis.Genome biology, 19(1):15.

Benchmarking virtual cell models for in-the-wild perturbation response Scanpy: large-scale single-cell gene expression data analysis.Genome biology, 19(1):15

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.639167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e70ec3f-e671-4870-8804-e498d2d93a5c · outbound

This paper cites anndata: Annotated data.BioRxiv, pages 2021–12.

Benchmarking virtual cell models for in-the-wild perturbation response anndata: Annotated data.BioRxiv, pages 2021–12

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.635648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 676cfd1a-edf3-4bb0-88bc-f4daa05b8cf4 · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130.

Benchmarking virtual cell models for in-the-wild perturbation response Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.584461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6557a3ff-b9b8-4c03-bdbd-332c91b9cd68 · outbound

This paper cites Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754.

Benchmarking virtual cell models for in-the-wild perturbation response Extended-connectivity fingerprints.Journal of chemical information and modeling, 50(5):742–754

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.642654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c025ae0a-ce9d-4350-aa35-c4e70b075243 · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response Disentanglement of single-cell data with biolord.Nature Biotechnology, 42(11):1678–1683

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.661804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation aee63897-82da-4474-98b4-35bda09b047f · outbound

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

Benchmarking virtual cell models for in-the-wild perturbation response Modelling cellular perturbations with the sparse additive mechanism shift variational autoencoder.Advances in Neural Information Processing Systems, 36:1–12

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.628824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 50137cab-29dd-4774-a58f-e0ce0ffd4322 · outbound

This paper cites Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery.

Benchmarking virtual cell models for in-the-wild perturbation response Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.615723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f32a15c1-10a0-416e-8124-b0177dce256c · outbound

This paper cites Modeling and predicting single-cell multi-gene perturbation responses with sclambda.bioRxiv.

Benchmarking virtual cell models for in-the-wild perturbation response Modeling and predicting single-cell multi-gene perturbation responses with sclambda.bioRxiv

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.537451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 824d7c0e-fbe9-49ea-befd-cbef7c1f03fa · outbound

This paper cites Genepert: Leveraging genept embeddings for gene perturbation prediction.bioRxiv.

Benchmarking virtual cell models for in-the-wild perturbation response Genepert: Leveraging genept embeddings for gene perturbation prediction.bioRxiv

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.570331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d191ba9-3737-42b1-8c50-63d2fdf30bc1 · outbound

This paper cites Predicting cellular responses to perturbation across diverse contexts with state.bioRxiv.

Benchmarking virtual cell models for in-the-wild perturbation response Predicting cellular responses to perturbation across diverse contexts with state.bioRxiv

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.529801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2a1f1ae2-a795-4001-a731-665d5a62dd7d · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information processing systems, 26.

Benchmarking virtual cell models for in-the-wild perturbation response Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information processing systems, 26

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.509372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3bf9203c-daaf-47c3-bfea-73140fbf9531 · outbound

This paper cites Interpolating between optimal transport and mmd using sinkhorn divergences.

Benchmarking virtual cell models for in-the-wild perturbation response Interpolating between optimal transport and mmd using sinkhorn divergences

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:53:59.497713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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