Paper Citation Record · LEDGER

Generating Synthetic Genotypes using Diffusion Models

As of 22 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.03278.

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

pith.paper-citation-record.v1
2412.03278 v3

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:39:04.206386Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 67e4dd83-19c5-4220-bc24-3101c6705e1b · outbound

This paper cites European Journal of Human Genetics, 26 0 (10): 0 1537--1546, 2018.

Generating Synthetic Genotypes using Diffusion Models European Journal of Human Genetics, 26 0 (10): 0 1537--1546, 2018

Reference 1

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Observation 36767eaa-1760-44b0-a883-3963f6d69709 · outbound

This paper cites Genome-ac-gan: Enhancing synthetic genotype generation through auxiliary classification.

Generating Synthetic Genotypes using Diffusion Models Genome-ac-gan: Enhancing synthetic genotype generation through auxiliary classification

Reference 2

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Observation b4341582-ad3c-4dd0-9d80-c5bcbabb427b · outbound

This paper cites Imputation of exome sequence variants into population-based samples and blood-cell-trait-associated loci in african americans: Nhlbi go exome sequencing project.

Generating Synthetic Genotypes using Diffusion Models Imputation of exome sequence variants into population-based samples and blood-cell-trait-associated loci in african americans: Nhlbi go exome sequencing project

Reference 3

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Observation 3cbfb7a6-54b8-4224-8901-6c96a2c34bc7 · outbound

This paper cites Dirichlet diffusion score model for biological sequence generation.

Generating Synthetic Genotypes using Diffusion Models Dirichlet diffusion score model for biological sequence generation

Reference 4

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This paper cites an unresolved cited work.

Generating Synthetic Genotypes using Diffusion Models Unresolved cited work

Reference 5

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Observation fa96f723-b6d4-443d-b9a2-3aa94850bfb8 · outbound

This paper cites Generating realistic artificial human genomes using adversarial autoencoders.

Generating Synthetic Genotypes using Diffusion Models Generating realistic artificial human genomes using adversarial autoencoders

Reference 6

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Observation cfc6fa49-33ed-4fd4-850d-e8e2d5411708 · outbound

This paper cites Accurate proteome-wide missense variant effect prediction with alphamissense.

Generating Synthetic Genotypes using Diffusion Models Accurate proteome-wide missense variant effect prediction with alphamissense

Reference 7

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Observation 9592bce3-1cc8-43ef-96a2-45b9a3e7c3d7 · outbound

This paper cites A global reference for human genetic variation.

Generating Synthetic Genotypes using Diffusion Models A global reference for human genetic variation

Reference 8

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This paper cites Tractable and expressive generative models of genetic variation data.

Generating Synthetic Genotypes using Diffusion Models Tractable and expressive generative models of genetic variation data

Reference 9

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Observation 9d795ef7-2d3c-47fd-9f6b-7714a174ee6d · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding.

Generating Synthetic Genotypes using Diffusion Models BERT: pre-training of deep bidirectional transformers for language understanding

Reference 10

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Observation 8ab516a2-825d-4f95-9f9b-35c5a54a4a72 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Generating Synthetic Genotypes using Diffusion Models Diffusion models beat gans on image synthesis

Reference 11

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Source-reported events for the cited work

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Observation e16c940e-9059-401d-8baf-1c96a35c8dd4 · outbound

This paper cites Detection of long repeat expansions from pcr-free whole-genome sequence data.

Generating Synthetic Genotypes using Diffusion Models Detection of long repeat expansions from pcr-free whole-genome sequence data

Reference 12

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Observation d5d1109f-bb91-4e8f-b360-8938d288d51f · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Generating Synthetic Genotypes using Diffusion Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation 2fdec0f6-c2f2-4994-826a-3c8eba684e6c · outbound

This paper cites Diffusion models in bioinformatics and computational biology.

Generating Synthetic Genotypes using Diffusion Models Diffusion models in bioinformatics and computational biology

Reference 14

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Observation 4a60eb25-8bed-4b0d-a27c-58ac9b75afd5 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Generating Synthetic Genotypes using Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 15

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Observation 3c2a8978-ff26-4293-b0ee-00203188754b · outbound

This paper cites Classifier-Free Diffusion Guidance.

Generating Synthetic Genotypes using Diffusion Models Classifier-Free Diffusion Guidance

Reference 16

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Observation 3d731e59-1c90-4005-bdeb-825b886b219f · outbound

This paper cites Denoising diffusion probabilistic models.

Generating Synthetic Genotypes using Diffusion Models Denoising diffusion probabilistic models

Reference 17

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Observation b04b83f8-7ee5-442e-ac11-3f0278372ef0 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Generating Synthetic Genotypes using Diffusion Models Highly accurate protein structure prediction with alphafold

Reference 18

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Observation 396be6e3-5954-4b8c-9ef0-6951b066f307 · outbound

This paper cites DiscDiff: Latent Diffusion Model for DNA Sequence Generation.

Generating Synthetic Genotypes using Diffusion Models DiscDiff: Latent Diffusion Model for DNA Sequence Generation

Reference 19

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This paper cites Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks.

Generating Synthetic Genotypes using Diffusion Models Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks

Reference 20

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Observation 98140148-f0c1-4ead-8c76-77fda2393b13 · outbound

This paper cites Umap: Uniform manifold approximation and projection.

Generating Synthetic Genotypes using Diffusion Models Umap: Uniform manifold approximation and projection

Reference 21

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Observation 20203ed1-7748-4b6a-9bf0-9bdfbf1e48b8 · outbound

This paper cites Baccus, and Chris Ré.

Generating Synthetic Genotypes using Diffusion Models Baccus, and Chris Ré

Reference 22

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Observation d1d40159-36ca-4b58-8b90-f30e7dc27796 · outbound

This paper cites Generative moment matching networks for genotype simulation.

Generating Synthetic Genotypes using Diffusion Models Generative moment matching networks for genotype simulation

Reference 23

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Observation 6972c2da-b007-4714-951f-c1e3e7d07653 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Generating Synthetic Genotypes using Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 24

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Observation 1dff94bf-116f-4070-b305-325878385d4e · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Generating Synthetic Genotypes using Diffusion Models U-net: Convolutional networks for biomedical image segmentation

Reference 25

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Observation 4e545ee5-eef7-4d76-9484-6c9e439ee51f · outbound

This paper cites Improved techniques for training gans.

Generating Synthetic Genotypes using Diffusion Models Improved techniques for training gans

Reference 26

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Observation d9ab5e79-ec67-461b-a7b5-22ea5f68d10a · outbound

This paper cites Designing dna with tunable regulatory activity using discrete diffusion.

Generating Synthetic Genotypes using Diffusion Models Designing dna with tunable regulatory activity using discrete diffusion

Reference 27

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Observation b9d6b1a7-7ced-4892-826a-0397b254b6b8 · outbound

This paper cites Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.

Generating Synthetic Genotypes using Diffusion Models Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling

Reference 28

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Observation 8c34b685-36ba-4fd8-87d6-33144c7e7868 · outbound

This paper cites Dna-diffusion: Leveraging generative models for controlling chromatin accessibility and gene expression via synthetic regulatory elements.

Generating Synthetic Genotypes using Diffusion Models Dna-diffusion: Leveraging generative models for controlling chromatin accessibility and gene expression via synthetic regulatory elements

Reference 29

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Observation 510ae02c-fdb9-45f6-b3de-82dd8f18683f · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Generating Synthetic Genotypes using Diffusion Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 31

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Observation 00b29b96-f751-4c18-a82e-94656b94aa86 · outbound

This paper cites Understanding and mitigating copying in diffusion models.

Generating Synthetic Genotypes using Diffusion Models Understanding and mitigating copying in diffusion models

Reference 32

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Observation 3b4c9939-0953-4c3a-ad4c-f7408f04e68c · outbound

This paper cites Denoising diffusion implicit models, 2022.

Generating Synthetic Genotypes using Diffusion Models Denoising diffusion implicit models, 2022

Reference 33

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Observation 928238e8-7337-4fae-a05d-5c08648deedc · outbound

This paper cites Towards creating longer genetic sequences with gans: Generation in principal component space.

Generating Synthetic Genotypes using Diffusion Models Towards creating longer genetic sequences with gans: Generation in principal component space

Reference 34

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Observation 3d6f9db4-d10c-45f4-813d-926d40511ce4 · outbound

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Generating Synthetic Genotypes using Diffusion Models Visualizing data using t-sne

Reference 35

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Observation c53375c9-5eff-4a33-952c-0b3ea0a8861c · outbound

This paper cites HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes.

Generating Synthetic Genotypes using Diffusion Models HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes

Reference 36

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Observation f22ee2ea-3b6b-47a3-b54d-b3612bb34dd0 · outbound

This paper cites Discovery of a structural class of antibiotics with explainable deep learning.

Generating Synthetic Genotypes using Diffusion Models Discovery of a structural class of antibiotics with explainable deep learning

Reference 37

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Observation 447ea457-2121-4edb-b8b8-aa870e7771bf · outbound

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Generating Synthetic Genotypes using Diffusion Models Privacy preserving synthetic health data

Reference 38

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Observation bdf044bb-14d6-438c-aafd-22b7b1a3eb3e · outbound

This paper cites Creating artificial human genomes using generative neural networks.

Generating Synthetic Genotypes using Diffusion Models Creating artificial human genomes using generative neural networks

Reference 39

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Observation 3a7638c4-a9df-4536-bbf1-4258f689c680 · outbound

This paper cites Deep convolutional and conditional neural networks for large-scale genomic data generation.

Generating Synthetic Genotypes using Diffusion Models Deep convolutional and conditional neural networks for large-scale genomic data generation

Reference 40

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Observation 46b7485a-2485-4056-955f-7669b7ae884b · outbound

This paper cites Dnagpt: a generalized pretrained tool for multiple dna sequence analysis tasks.

Generating Synthetic Genotypes using Diffusion Models Dnagpt: a generalized pretrained tool for multiple dna sequence analysis tasks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:39:04.767823Z

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Observation bf49a572-21ac-46a0-b35f-1d2e4bf6e86a · outbound

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Generating Synthetic Genotypes using Diffusion Models write newline

Reference 42

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Generating Synthetic Genotypes using Diffusion Models @esa (Ref

Reference 43

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Observation 5a390c4d-d3ad-4ccf-a9a1-77a6758ac900 · outbound

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Generating Synthetic Genotypes using Diffusion Models Unresolved cited work

Reference 44

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Observation ae325231-1f86-4ec3-a73c-86fca608c8fe · outbound

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Generating Synthetic Genotypes using Diffusion Models oup-authoring-template.cls

Reference 45

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Pith citing papers

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