The promise of machine learning approaches to capture cellular senescence heterogeneity

Imanol Duran^{1

2}, Cleo L Bishop³, Jesús Gil^{1

2}, Ryan Wallis⁴

Affiliations

¹ MRC Laboratory of Medical Sciences (LMS), London, UK.
² Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK.
³ Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
⁴ Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK. r.wallis@qmul.ac.uk.

Imanol Duran et al. Nat Aging. 2024 Sep.

. 2024 Sep;4(9):1167-1170.

doi: 10.1038/s43587-024-00703-2.

Imanol Duran^{1

2}, Cleo L Bishop³, Jesús Gil^{1

2}, Ryan Wallis⁴

¹ MRC Laboratory of Medical Sciences (LMS), London, UK.
² Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London, UK.
³ Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
⁴ Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK. r.wallis@qmul.ac.uk.

No abstract available

References

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1. Tao, W., Yu, Z. & Han, J. J. Cell Metab. 36, 1126–1143.e5 (2024). - PubMed - DOI
1. SenNet Consortium. Nat. Aging 2, 1090–1100 (2022). - PMC - DOI
1. Smer-Barreto, V. et al. Nat. Commun. 14, 3445 (2023). - PubMed - PMC - DOI