Evidence map›Paper›PMID 41959091›Full record

ArticlebioRxiv : the preprint server for biology2026

Temporal AI model predicts drivers of cell state trajectories across human aging.

Javier Gómez Ortega, Rangarajan D Nadadur, Akira Kunitomi, Steven Kothen-Hill, Julian U G Wagner, Sarp D Kurtoglu, Bumjoon Kim, Madigan M Reid, Thomas Lu, Kaho Washizu and 15 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Javier Gómez OrtegaGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.ORCID 0000-0001-7821-4760
Rangarajan D NadadurGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Akira KunitomiGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Steven Kothen-HillNVIDIA, San Clara, CA, USA.
Julian U G WagnerInstitute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt; German Center for Cardiovascular Research, Partner Site Rhein-Main, Frankfurt, Germany.
Sarp D KurtogluGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Bumjoon KimGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Madigan M ReidGladstone Institute of Neurological Disease, San Francisco, CA, USA.
Thomas LuGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Kaho WashizuGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Lukas ZandersInstitute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt; German Center for Cardiovascular Research, Partner Site Rhein-Main, Frankfurt, Germany.
Han ChenGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Yujie ZhangGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Sarah AnchetaGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Sara LichtargeGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
William A JohnsonGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Chris ThompsonGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Dereck M PhanGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Alexis J CombesDepartment of Pathology, UCSF, San Francisco, CA, USA.
Andrew C YangGladstone Institute of Neurological Disease, San Francisco, CA, USA.
Neha TadimetiNVIDIA, San Clara, CA, USA.
Stefanie DimmelerInstitute of Cardiovascular Regeneration, Centre for Molecular Medicine, Goethe University Frankfurt; German Center for Cardiovascular Research, Partner Site Rhein-Main, Frankfurt, Germany.
Shinya YamanakaGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Michael AlexanianGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
Christina V TheodorisGladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.

Funding

Transfer learning leveraging large-scale transcriptomics to map disrupted gene networks in cardiovascular diseaseDP5OD036170 · OD · J. DAVID GLADSTONE INSTITUTES · PI Christina Vicky Theodoris · 2023 to 2026
$2.4M
Epigenomic mechanisms of cardiac fibrosis and heart failureR01HL175312 · NHLBI · J. DAVID GLADSTONE INSTITUTES · PI Michael Alexanian · 2025 to 2026
$1.6M
Chromatin Regulation of Immune Cells in Myocardial InflammationR01HL181372 · NHLBI · J. DAVID GLADSTONE INSTITUTES · PI Michael Alexanian · 2025 to 2026
$1.6M
NHLBI NIH HHS R01 HL175312NHLBI NIH HHS R01 HL181372NIH HHS DP5 OD036170
6 · The paper itself

Abstract

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

Identifiers

PMID41959091
PMCPMC13060336

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.