Paper Citation Record · LEDGER

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2607.06583.

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pith.paper-citation-record.v1
2607.06583 v1

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measured 37 of 37 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-11T22:32:45.812013Z

measured 37 of 37 standing notices

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37 of 37 outbound references displayed

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Outbound references

Observation 15d8d449-3a16-4794-a5fd-53d73cbede30 · outbound

This paper cites Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming.Cell, 176(4):928–943, 2019.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming.Cell, 176(4):928–943, 2019

Reference 1

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Observation 15d1798c-f23f-40f9-9936-deca65b98195 · outbound

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

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Learning single- cell perturbation responses using neural optimal transport.Nature methods, 20(11):1759–1768, 2023

Reference 2

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Observation 2702f868-759d-4790-bafd-8c50c8b571a8 · outbound

This paper cites Towards a mathematical theory of trajectory inference.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Towards a mathematical theory of trajectory inference

Reference 3

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Observation a803897b-3551-4a1f-b791-6c6094de134f · outbound

This paper cites Optimal transport for single-cell and spatial omics.Nature Reviews Methods Primers, 4(1):58, 2024.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Optimal transport for single-cell and spatial omics.Nature Reviews Methods Primers, 4(1):58, 2024

Reference 4

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Observation 1dd59c9b-95ca-41ef-b787-1f0787c1579f · outbound

This paper cites Dna methylation and healthy human aging.Aging cell, 14(6):924–932, 2015.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Dna methylation and healthy human aging.Aging cell, 14(6):924–932, 2015

Reference 5

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Observation dae7af0f-763f-406b-a6c3-7ea93cdaf88d · outbound

This paper cites Dna methylation clocks in aging: categories, causes, and consequences.Molecular cell, 71(6):882–895, 2018.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Dna methylation clocks in aging: categories, causes, and consequences.Molecular cell, 71(6):882–895, 2018

Reference 6

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Observation 079c4dd0-9bca-4835-8125-17ad76b77cfa · outbound

This paper cites Dna methylation age of human tissues and cell types.Genome biology, 14(10):3156, 2013.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Dna methylation age of human tissues and cell types.Genome biology, 14(10):3156, 2013

Reference 7

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Observation d8e13723-204f-4b8c-9491-288724e43201 · outbound

This paper cites Genome-wide methylation profiles reveal quantitative views of human aging rates.Molecular cell, 49(2):359–367, 2013.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Genome-wide methylation profiles reveal quantitative views of human aging rates.Molecular cell, 49(2):359–367, 2013

Reference 8

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Observation 1bdeed0b-004b-4074-a8ae-3425235c4b5a · outbound

This paper cites An epigenetic biomarker of aging for lifespan and healthspan.Aging (albany NY), 10(4):573, 2018.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data An epigenetic biomarker of aging for lifespan and healthspan.Aging (albany NY), 10(4):573, 2018

Reference 9

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Observation 82be9a67-84e1-45cc-96a9-ddfb7c2de0ac · outbound

This paper cites Dna methylation grimage strongly predicts lifespan and healthspan.Aging (albany NY), 11(2):303, 2019.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Dna methylation grimage strongly predicts lifespan and healthspan.Aging (albany NY), 11(2):303, 2019

Reference 10

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Observation a2849bd6-4f5d-46c3-bf41-bcfed9ecbbd2 · outbound

This paper cites Human epigenetic ageing is logarithmic with time across the entire lifespan.Epigenetics, 14(9):912–926, 2019.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Human epigenetic ageing is logarithmic with time across the entire lifespan.Epigenetics, 14(9):912–926, 2019

Reference 11

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Observation 5fb6a0d4-bed1-4431-8473-3965875bb2cb · outbound

This paper cites Fractional calculus in epigenet- ics: Modelling dna methylation dynamics using mittag–leffler function.Fractal and Fractional, 9(9):616, 2025.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Fractional calculus in epigenet- ics: Modelling dna methylation dynamics using mittag–leffler function.Fractal and Fractional, 9(9):616, 2025

Reference 12

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Observation d792990e-8b4a-4854-9b41-226613ae6b73 · outbound

This paper cites an unresolved cited work.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Unresolved cited work

Reference 13

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Observation c4044616-8380-4d9f-97c5-1e6b76c89fbe · outbound

This paper cites A mathematical model which examines age-related stochastic fluctuations in dna maintenance methylation.Experimental Gerontology, 156:111623, 2021.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data A mathematical model which examines age-related stochastic fluctuations in dna maintenance methylation.Experimental Gerontology, 156:111623, 2021

Reference 14

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Observation ba3d9d29-d827-4897-a841-b71ec2f7adf9 · outbound

This paper cites Dna methylation age of blood predicts all-cause mortality in later life.Genome biology, 16(1):25, 2015.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Dna methylation age of blood predicts all-cause mortality in later life.Genome biology, 16(1):25, 2015

Reference 15

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Observation fa37e7ab-e391-435f-9e17-9609d5528f80 · outbound

This paper cites Accounting for cellular heterogeneity is critical in epigenome-wide association studies.Genome biology, 15(2):R31, 2014.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Accounting for cellular heterogeneity is critical in epigenome-wide association studies.Genome biology, 15(2):R31, 2014

Reference 16

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Observation 5097aa76-9ce4-4eaa-9c33-5781a5925919 · outbound

This paper cites A computational fluid mechanics solution to the monge-kantorovich mass transfer problem.Numerische Mathematik, 84(3):375–393, 2000.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data A computational fluid mechanics solution to the monge-kantorovich mass transfer problem.Numerische Mathematik, 84(3):375–393, 2000

Reference 17

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Observation f128d5bd-33c6-4ab3-ae72-1ba041717252 · outbound

This paper cites A survey of the Schr\"odinger problem and some of its connections with optimal transport.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data A survey of the Schr\"odinger problem and some of its connections with optimal transport

Reference 18

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Observation cfbfba72-9284-4e53-9cad-3f5324461dec · outbound

This paper cites The most likely evolution of diffusing and vanishing particles: Schrodinger bridges with unbalanced marginals.SIAM Journal on Control and Optimization, 60(4):2016–2039, 2022.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data The most likely evolution of diffusing and vanishing particles: Schrodinger bridges with unbalanced marginals.SIAM Journal on Control and Optimization, 60(4):2016–2039, 2022

Reference 19

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Observation 639b294b-81c2-4765-be4e-8c8e81273616 · outbound

This paper cites An interpolat- ing distance between optimal transport and fisher–rao metrics.F oundations of Computational Mathematics, 18(1):1–44, 2018.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data An interpolat- ing distance between optimal transport and fisher–rao metrics.F oundations of Computational Mathematics, 18(1):1–44, 2018

Reference 20

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Observation c62e109f-ed81-4c39-970e-cafd690250b7 · outbound

This paper cites Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport

Reference 21

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Observation 2edbcf36-546f-4f14-a7f2-270db3a15f3a · outbound

This paper cites Auto-encoding variational bayes.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Auto-encoding variational bayes

Reference 22

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Observation 47407f7a-a522-4429-81e4-efb16f8abd36 · outbound

This paper cites β-vae: Learning basic visual con- cepts with a constrained variational framework.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data β-vae: Learning basic visual con- cepts with a constrained variational framework

Reference 23

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Observation dbb5a4e6-a854-4dfa-b4fb-7139631937b1 · outbound

This paper cites A pan-tissue dna- methylation epigenetic clock based on deep learning.npj Aging, 8(1):4, 2022.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data A pan-tissue dna- methylation epigenetic clock based on deep learning.npj Aging, 8(1):4, 2022

Reference 24

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Observation ed84a9c8-b2a8-402a-98e4-15ddbe69cc3d · outbound

This paper cites USAF school of Aviation Medicine, 1985.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data USAF school of Aviation Medicine, 1985

Reference 25

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Observation 18aaf9ac-9a77-445c-b945-f012c7d9e5e3 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014

Reference 26

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Observation 412baa72-f039-4c8c-8d44-ff9912e75d0e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Adam: A Method for Stochastic Optimization

Reference 27

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Observation ee155cfd-4875-46f2-9331-d8c055e51189 · outbound

This paper cites Epige- netic differences arise during the lifetime of monozygotic twins.Proceedings of the National Academy of Sciences, 102(30):10604–10609, 2005.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Epige- netic differences arise during the lifetime of monozygotic twins.Proceedings of the National Academy of Sciences, 102(30):10604–10609, 2005

Reference 28

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Observation 3f1b0083-aef4-4af2-a5f5-bd6c287cd2fa · outbound

This paper cites The rate of epigenetic drift scales with maximum lifespan across mammals.Nature Communications, 14(1):7731, 2023.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data The rate of epigenetic drift scales with maximum lifespan across mammals.Nature Communications, 14(1):7731, 2023

Reference 29

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Observation c3ef08c9-e260-4316-9c0e-3eace622eaa1 · outbound

This paper cites Making sense of the ageing methylome.Nature Reviews Genetics, 23(10):585–605, 2022.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Making sense of the ageing methylome.Nature Reviews Genetics, 23(10):585–605, 2022

Reference 30

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Observation 09640734-3e98-4d2a-b2a1-bdffc4aa1a76 · outbound

This paper cites Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Reference 31

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Observation b20f820f-be46-4717-97e5-3175be72572d · outbound

This paper cites Hallmarks of aging: An expanding universe.Cell, 186(2):243–278, 2023.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Hallmarks of aging: An expanding universe.Cell, 186(2):243–278, 2023

Reference 32

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Observation f8635085-2dbc-45b3-a95b-58d9949dddd9 · outbound

This paper cites Human aging- associated dna hypermethylation occurs preferentially at bivalent chromatin domains.Genome research, 20(4):434, 2010.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Human aging- associated dna hypermethylation occurs preferentially at bivalent chromatin domains.Genome research, 20(4):434, 2010

Reference 33

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Observation 0a1ca637-8497-463f-999f-c6ea7036b0ae · outbound

This paper cites Decline in genomic dna methy- lation through aging in a cohort of elderly subjects.Mechanisms of ageing and development, 130(4):234–239, 2009.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Decline in genomic dna methy- lation through aging in a cohort of elderly subjects.Mechanisms of ageing and development, 130(4):234–239, 2009

Reference 34

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Observation 95aebf0b-f4b5-4185-b7ba-05d3689a5f89 · outbound

This paper cites Senescent cells harbour features of the cancer epigenome.Nature cell biology, 15(12):1495– 1506, 2013.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Senescent cells harbour features of the cancer epigenome.Nature cell biology, 15(12):1495– 1506, 2013

Reference 35

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Observation 051ac3cb-d4d9-42dd-b853-565fe36f4b62 · outbound

This paper cites Charting a dynamic dna methylation landscape of the human genome.Nature, 500(7463):477– 481, 2013.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Charting a dynamic dna methylation landscape of the human genome.Nature, 500(7463):477– 481, 2013

Reference 36

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Observation d0d7f856-30d0-4bd2-a00e-5ee17a37b4a1 · outbound

This paper cites Aging and epigenetic drift: a vicious cycle.The Journal of clinical investigation, 124(1):24–29, 2014.

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data Aging and epigenetic drift: a vicious cycle.The Journal of clinical investigation, 124(1):24–29, 2014

Reference 37

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