Evidence map›Paper›PMID 40631080›Full record

ArticlebioRxiv : the preprint server for biology2025

An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation.

Anjun Ma, Hao Cheng, Natalia Del Pilar Vanegas, Ahmed Ghobashi, Hu Chen, Jhonny Rodriguez, Lorena Rosas, Cankun Wang, Jianming Shao, Yi Jiang and 14 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

24 authors.

Anjun MaDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Hao ChengDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Natalia Del Pilar VanegasDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Ohio State University, Columbus, OH, USA.
Ahmed GhobashiDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Hu ChenDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Jhonny RodriguezDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Ohio State University, Columbus, OH, USA.
Lorena RosasDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Ohio State University, Columbus, OH, USA.
Cankun WangDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Jianming ShaoDepartment of Molecular and Human Genetics, Baylor College of Medicine, 1 Baylor Plaza, Houston, Texas, 77030 USA.
Yi JiangDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Xiaoying WangDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Irfan RahmanEnvironmental Medicine, University of Rochester, Rochester, NY, USA.
Jose Lugo-MartinezRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Dongmei LiDepartment of Clinical and Translational Research, University of Rochester Medical Center, Rochester, NY, USA.ORCID 0000-0001-9140-2483
Gloria S PryhuberDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA.
Ziv Bar-JosephRay and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Oliver EickelbergCenter of Lung Aging and Regeneration, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Melanie KönigshoffCenter of Lung Aging and Regeneration, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Dongjun ChungDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Rui ChenDepartment of Ophthalmology and Vision Science, University of California Irvine School of Medicine, Irvine, CA, USA.
Toren FinkelAging Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Ana MoraDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Ohio State University, Columbus, OH, USA.
Mauricio RojasDorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Ohio State University, Columbus, OH, USA.
Qin MaDepartment of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.

Funding

TriState SenNET (Lung and Heart) Tissue Map and Atlas consortiumU54AG075931 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TOREN FINKEL, Melanie Koenigshoff · 2021 to 2026
$14.0M
Statistical Power Analysis Framework for Multi-Sample and Cross-Platform Spatial Omics ExperimentsR01GM152585 · NIGMS · OHIO STATE UNIVERSITY · PI Dongjun Chung, Qin Ma · 2024 to 2026
$1.2M
NIA NIH HHS U54 AG075931NIGMS NIH HHS R01 GM152585
6 · The paper itself

Abstract

Characterizing senescent cells and identifying corresponding senescence-associated genes within complex tissues is critical for our understanding of aging and age-related diseases. We present DeepSAS, an intrinsic-hoc framework for elucidating the heterogeneity encoded in senescent cells and their associated genes from single-cell RNA-seq data using deep graph representation learning. Applied to both healthy eye cell atlas and in-house idiopathic pulmonary fibrosis datasets with Xenium spatial transcriptomics validation, DeepSAS reveals robust and biologically grounded senotypes and demonstrates superior benchmarking performance compared with existing methods.

Identifiers

PMID40631080
PMCPMC12236723

What OpenQuestion holds

Textmetadata
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.