PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging-Reference-Cited by-同舟云学术

PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging

Published:2018-11-09 Issue:11 Volume:10 Page:3249-3259
ISSN:1945-4589
Container-title:Aging
language:en
Short-container-title:Aging

Author:

Bobrov Eugene¹²,Georgievskaya Anastasia¹³,Kiselev Konstantin¹,Sevastopolsky Artem¹⁴,Zhavoronkov Alex⁵⁶⁷,Gurov Sergey²,Rudakov Konstantin³,del Pilar Bonilla Tobar Maria⁸,Jaspers Sören⁸,Clemann Sven⁸

Affiliation:

1. HautAI OU, Tallinn, Estonia

2. Lomonosov Moscow State University, Moscow, Russia

3. Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia

4. Skolkovo Institute of Science and Technology, Moscow, Russia

5. Insilico Medicine, Rockville, MD 20850, USA

6. The Buck Institute for Research on Aging, Novato, CA 94945, USA

7. The Biogerontology Research Foundation, London, UK

8. Beiersdorf AG, Hamburg, Germany

Publisher

Impact Journals, LLC

Subject

Cell Biology,Ageing

Cited by 62 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of AI in biological age prediction;Current Opinion in Structural Biology;2024-04

2. MyoV: a deep learning-based tool for the automated quantification of muscle fibers;Briefings in Bioinformatics;2024-01-22

3. Nurturing longevity through natural compounds: Where do we stand, and where do we go?;Food Frontiers;2024-01-10

4. TIME-seq reduces time and cost of DNA methylation measurement for epigenetic clock construction;Nature Aging;2024-01-10

5. The ticking of aging clocks;Trends in Endocrinology & Metabolism;2024-01