Advancing biological understanding of cellular senescence with computational multiomics

By afschroeder • September 16, 2025

Li S, Agudelo Garcia PA, Aliferis C, Becich MJ, Calyeca J, Cosgrove BD, Elisseeff J, Farzad N, Fertig EJ, Glass C, Gu L, Hu Q, Ji Z, Königshoff M, LeBrasseur NK, Li D, Ma A, Ma Q, Menon V, Mitchell JT, Mora AL, Nagaraj S, Nelson AC, Niedernhofer LJ, Rojas M, Taha HB, Wang J, Wang S, Wu PH, Xie J, Xu M, Yu M, Zhang X, Zhao Y, Adams PD, Aguayo-Mazzucato C, Baker DJ, Benz C, Bernlohr DA, Bueno M, Chen J, Childs BG, Chuang JH, Chung D, Dileepan M, Ding L, Dong M, Duncan F, Enninful A, Flynn WF, Franco AC, Furman D, Garovic V, Halene S, Herman AB, Hertzel AV, Iwasaki K, Jeon H, Kang JW, Karmakar S, Kirkland JL, Korstanje R, Kummerfeld E, Lee JH, Liu Y, Lu Y, Lugo-Martinez J, Martini H, Melov S, Musi N, Passos JF, Peters ST, Rahman I, Ramasamy R, Rindone AN, Robbins PD, Robson P, Rodriguez-Lopez J, Rosas L, Rosenthal N, Schafer MJ, Schilling B, Schmidt EL, Schneider K, Sengupta K, Shu J, So PTC, Sun L, Tchkonia T, Teneche MG, Vanegas N, Wang J, Xie J, Yin S, Zhang K, Zhu Q, Fan R; SenNet Consortium. Advancing biological understanding of cellular senescence with computational multiomics. Nat Genet. 2025 Sep 15. doi: 10.1038/s41588-025-02314-y. Epub ahead of print. PMID: 40954249.

Tags: Cedars Sinai DoM, Endocrinology, Publications

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