Integrative Machine Learning Analysis of Programmed Cell Death Pathways Identifies Novel Diagnostic Biomarkers for Atrial Fibrillation

Our integrative framework reveals PCD-linked remodeling in AF and nominates SGPL1, NPC2, PTGDS, and RCAN1 as candidate diagnostic biomarkers, providing a PCD-based nomogram and risk score that may inform patient stratification and hypothesis-generating targeted interventions.

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Publication Details

Journal
Journal of Inflammation Research
Published
2025-12-01
DOI
https://doi.org/10.2147/jir.s568171
Primary Topic
Atrial Fibrillation Management and Outcomes
Type
article
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article

Integrative Machine Learning Analysis of Programmed Cell Death Pathways Identifies Novel Diagnostic Biomarkers for Atrial Fibrillation

Yangyang Zhao, Zhenwei Xia, Di Xie, Hongbo Peng
Journal of Inflammation Research
Atrial Fibrillation Management and Outcomes
article

Integrative Machine Learning Analysis of Programmed Cell Death Pathways Identifies Novel Diagnostic Biomarkers for Atrial Fibrillation

Yangyang Zhao, Zhenwei Xia, Di Xie, Hongbo Peng
article en

Abstract

Our integrative framework reveals PCD-linked remodeling in AF and nominates SGPL1, NPC2, PTGDS, and RCAN1 as candidate diagnostic biomarkers, providing a PCD-based nomogram and risk score that may inform patient stratification and hypothesis-generating targeted interventions.

Journal of Inflammation ResearchVol. Volume 18
Dalian Municipal Central Hospital (CN)
Good health and well-being
Openalex Percentile: Top 30%
Atrial Fibrillation Management and Outcomes
DOIOpen Access PDFOpenAlex
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Integrative Machine Learning Analysis of Programmed Cell Death Pathways Identifies Novel Diagnostic Biomarkers for Atrial Fibrillation — Yangyang Zhao, Zhenwei Xia, et al. · Journal of Inflammation Research (2025) | TGRS Research Map | TGRS