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Paper Citation Record · LEDGER

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2411.12010.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.12010 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:06:15.669669Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 5953c39d-6d14-4d27-af38-ab11019ce1c2 · outbound

This paper cites Deep Learning-Based Predictions of Gene Perturbation Effects Do Not yet Outperform Simple Linear Methods.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Deep Learning-Based Predictions of Gene Perturbation Effects Do Not yet Outperform Simple Linear Methods

Reference 1

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Observation 1ec29d19-d5f8-403e-8ce6-e8e15d9da9be · outbound

This paper cites Learning to Make Decisions via Submodular Regularization.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Learning to Make Decisions via Submodular Regularization

Reference 2

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Observation eb4cf77e-d307-40c3-861e-ac0f779a7652 · outbound

This paper cites Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Combinatorial Drug Therapy for Cancer in the Post-Genomic Era

Reference 3

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Observation 84609966-26c0-49c2-86c7-df7664c85745 · outbound

This paper cites Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder

Reference 4

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Observation 7aa88323-7b7b-417c-ba6f-cdc10032ff8c · outbound

This paper cites RECOVER Identifies Synergistic Drug Combinations in Vitro through Sequential Model Optimization.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space RECOVER Identifies Synergistic Drug Combinations in Vitro through Sequential Model Optimization

Reference 5

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Observation 115687cf-9391-4114-bef7-d82b7c112346 · outbound

This paper cites On Initial Pools for Deep Active Learning.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space On Initial Pools for Deep Active Learning

Reference 6

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Observation fc979d25-5965-4e1b-b9e3-9d5ba9db6607 · outbound

This paper cites scGPT: Toward Building a Foundation Model for Single-Cell Multi-Omics Using Generative AI.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space scGPT: Toward Building a Foundation Model for Single-Cell Multi-Omics Using Generative AI

Reference 7

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Observation f01fd405-0e86-4e66-9da9-36b197ce3163 · outbound

This paper cites Active Machine Learning Helps Drug Hunters Tackle Biology.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Active Machine Learning Helps Drug Hunters Tackle Biology

Reference 8

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Observation d2cdcf92-7164-4299-879c-2c2ab45e7b3a · outbound

This paper cites A Tutorial on Bayesian Optimization.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space A Tutorial on Bayesian Optimization

Reference 9

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Observation b41eef21-f936-4c01-a135-0859a10e6486 · outbound

This paper cites Season combinatorial intervention predictions with Salt & Peper.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Season combinatorial intervention predictions with Salt & Peper

Reference 10

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Observation 60606f92-1015-4284-878b-8d099b1a9121 · outbound

This paper cites Systematically Characterizing the Roles of E3-Ligase Family Members in Inflammatory Responses with Massively Parallel Perturb-Seq.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Systematically Characterizing the Roles of E3-Ligase Family Members in Inflammatory Responses with Massively Parallel Perturb-Seq

Reference 11

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Observation 39f025f0-4317-4dc4-baa7-7680d7ad136e · outbound

This paper cites Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

Reference 12

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Observation 1586c0ae-548b-4216-9dbf-f01e279bcecb · outbound

This paper cites Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19

Reference 13

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Observation 425e242e-dafa-43d0-a404-f7d68fa88d72 · outbound

This paper cites Large-Scale Foundation Model on Single-Cell Transcriptomics.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Large-Scale Foundation Model on Single-Cell Transcriptomics

Reference 14

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Observation 3fb99c5e-bf47-4fe3-be84-52ffa6ccf767 · outbound

This paper cites Mapping the Genetic Landscape of Human Cells.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Mapping the Genetic Landscape of Human Cells

Reference 15

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation f68b9c5f-9123-420d-9521-7bc05da48ff4 · outbound

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Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Unresolved cited work

Reference 16

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Observation cd093ca7-81ef-479e-b4fd-caec3fba74a5 · outbound

This paper cites Additivity Predicts the Efficacy of Most Approved Combination Therapies for Advanced Cancer.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Additivity Predicts the Efficacy of Most Approved Combination Therapies for Advanced Cancer

Reference 17

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Observation 0795d684-49ee-4fed-9c50-1c590c3a67bf · outbound

This paper cites Triple–Hormone-Receptor Agonist Retatrutide for Obesity — A Phase 2 Trial.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Triple–Hormone-Receptor Agonist Retatrutide for Obesity — A Phase 2 Trial

Reference 18

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Observation 6a20638d-09c1-47a4-b629-7f1151757756 · outbound

This paper cites Auto-Encoding Variational Bayes.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Auto-Encoding Variational Bayes

Reference 19

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Observation 1b7c3a0a-7107-44da-856a-38f570244140 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

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Observation 70af035a-9fcd-47de-810c-f7652ad47f10 · outbound

This paper cites DEUP: Direct Epistemic Uncertainty Prediction.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space DEUP: Direct Epistemic Uncertainty Prediction

Reference 21

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Observation 44ef0fc4-3622-4606-86d7-caab522dd199 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 22

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Observation e64c313d-42ed-4e66-8246-4a69bfff1bd3 · outbound

This paper cites Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling

Reference 23

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Observation 24900d03-86ab-4cc4-b9dc-ab772a73ee2d · outbound

This paper cites Predicting Cellular Responses to Complex Perturbations in High- throughput Screens.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Predicting Cellular Responses to Complex Perturbations in High- throughput Screens

Reference 24

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Observation a326fbc2-c291-4bbc-91f7-8198ebe246b9 · outbound

This paper cites Combination Therapy in Combating Cancer.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Combination Therapy in Combating Cancer

Reference 25

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Observation 6c78f986-a4b5-4fae-ad4d-0d0626e35774 · outbound

This paper cites Exploring Genetic Interaction Manifolds Constructed from Rich Single-Cell Phenotypes.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Exploring Genetic Interaction Manifolds Constructed from Rich Single-Cell Phenotypes

Reference 26

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Observation 6203291b-7f5e-4dec-81d1-8e8675484b8a · outbound

This paper cites Rationalizing Combination Therapies.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Rationalizing Combination Therapies

Reference 27

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Observation 8632047e-8050-420d-af34-b99e526fdb91 · outbound

This paper cites Toward a Foundation Model of Causal Cell and Tissue Biology with a Perturbation Cell and Tissue Atlas.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Toward a Foundation Model of Causal Cell and Tissue Biology with a Perturbation Cell and Tissue Atlas

Reference 28

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation ca676afd-eb8f-4a5d-843f-b97803f2b73e · outbound

This paper cites Predicting Transcriptional Outcomes of Novel Multigene Perturbations with GEARS.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Predicting Transcriptional Outcomes of Novel Multigene Perturbations with GEARS

Reference 29

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Observation e25f384f-f35b-416e-bf6a-7a6bd7c91a1a · outbound

This paper cites How May GIP Enhance the Therapeutic Efficacy of GLP-1?.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space How May GIP Enhance the Therapeutic Efficacy of GLP-1?

Reference 30

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Observation db0e24a6-9565-4e13-b632-0125a24218db · outbound

This paper cites Drug Combination Therapy for Emerging Viral Diseases.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Drug Combination Therapy for Emerging Viral Diseases

Reference 31

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 85c43600-85af-4f12-9888-b76628dee98c · outbound

This paper cites Mapping the Genetic Interaction Network of PARP Inhibitor Response.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Mapping the Genetic Interaction Network of PARP Inhibitor Response

Reference 32

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 89e58d02-1013-41d3-8b12-1068908cf041 · outbound

This paper cites Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors

Reference 33

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation b2cafa5c-bc21-4fb4-b869-e24245e30499 · outbound

This paper cites A Versatile CRISPR-Cas13d Platform for Multiplexed Transcriptomic Regulation and Metabolic Engineering in Primary Human T Cells.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space A Versatile CRISPR-Cas13d Platform for Multiplexed Transcriptomic Regulation and Metabolic Engineering in Primary Human T Cells

Reference 34

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c1c7dc98-c7f2-4100-ac3e-c722c5457887 · outbound

This paper cites Submodularity in Data Subset Selection and Active Learning.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Submodularity in Data Subset Selection and Active Learning

Reference 35

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raw_fallback, observed 2026-08-12T18:06:15.997152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T18:06:15.664544Z digest=sha256:f82f5cc0bdd1fa07e2626b837be1aac558b1bb9ea169c739cd717085a5f24d7a

Observation ec6e577a-9843-4bda-8a3f-d7fcba5f918d · outbound

This paper cites an unresolved cited work.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Unresolved cited work

Reference 1024

Resolution
malformed identifier
raw_fallback, observed 2026-08-12T18:06:15.982808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T18:06:15.669669Z digest=sha256:d16030bf467912a44120e7c27d535cd5e810f5f1b8401fc6d8db4890bb841ab4

Observation 1763ff18-2a29-4052-ae49-476273d8071f · outbound

This paper cites Engineered CRISPR-Cas12a for Higher-Order Combinatorial Chromatin Perturbations.

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space Engineered CRISPR-Cas12a for Higher-Order Combinatorial Chromatin Perturbations

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:06:16.170969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T18:06:15.578670Z digest=sha256:9daf57dd1d6d70ceddacfeb93a77a8d1348f24bea04f5e124f4dc71a87aecf38

Pith citing papers

No inbound Pith citation observations are available.