Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity

作者: Alex Zhavoronkov , Polina Mamoshina

DOI: 10.1016/J.TIPS.2019.05.004

关键词:

摘要: First published in 2016, predictors of chronological and biological age developed using deep learning (DL) are rapidly gaining popularity the aging research community. These clocks can be used a broad range applications pharmaceutical industry, spanning target identification, drug discovery, data economics, synthetic patient generation. We provide here brief overview recent advances this important subset, or perhaps superset, that have been artificial intelligence (AI).

参考文章(21)
David Warde-Farley, Yoshua Bengio, Ian J. Goodfellow, Sherjil Ozair, Aaron Courville, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, Generative Adversarial Networks arXiv: Machine Learning. ,(2014)
Alex Zhavoronkov, Charles R. Cantor, Methods for Structuring Scientific Knowledge from Many Areas Related to Aging Research PLoS ONE. ,vol. 6, pp. e22597- ,(2011) , 10.1371/JOURNAL.PONE.0022597
Gregory Hannum, Justin Guinney, Ling Zhao, Li Zhang, Guy Hughes, SriniVas Sadda, Brandy Klotzle, Marina Bibikova, Jian-Bing Fan, Yuan Gao, Rob Deconde, Menzies Chen, Indika Rajapakse, Stephen Friend, Trey Ideker, Kang Zhang, Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates Molecular Cell. ,vol. 49, pp. 359- 367 ,(2013) , 10.1016/J.MOLCEL.2012.10.016
Alex Zhavoronkov, Bhupinder Bhullar, Classifying aging as a disease in the context of ICD-11 Frontiers in Genetics. ,vol. 6, pp. 326- 326 ,(2015) , 10.3389/FGENE.2015.00326
Monica Sager, Nai Chien Yeat, Stefan Pajaro-Van der Stadt, Charlotte Lin, Qiuyin Ren, Jimmy Lin, Transcriptomics in cancer diagnostics: developments in technology, clinical research and commercialization Expert Review of Molecular Diagnostics. ,vol. 15, pp. 1589- 1603 ,(2015) , 10.1586/14737159.2015.1105133
Polina Mamoshina, Armando Vieira, Evgeny Putin, Alex Zhavoronkov, Applications of Deep Learning in Biomedicine Molecular Pharmaceutics. ,vol. 13, pp. 1445- 1454 ,(2016) , 10.1021/ACS.MOLPHARMACEUT.5B00982
Alexander Aliper, Sergey Plis, Artem Artemov, Alvaro Ulloa, Polina Mamoshina, Alex Zhavoronkov, Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data Molecular Pharmaceutics. ,vol. 13, pp. 2524- 2530 ,(2016) , 10.1021/ACS.MOLPHARMACEUT.6B00248
Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, Alex Zhavoronkov, Deep biomarkers of human aging: Application of deep neural networks to biomarker development Aging (Albany NY). ,vol. 8, pp. 1021- 1033 ,(2017) , 10.18632/AGING.100968
Daniel A. Petkovich, Dmitriy I. Podolskiy, Alexei V. Lobanov, Sang-Goo Lee, Richard A. Miller, Vadim N. Gladyshev, Using DNA Methylation Profiling to Evaluate Biological Age and Longevity Interventions Cell Metabolism. ,vol. 25, pp. 954- 960.e6 ,(2017) , 10.1016/J.CMET.2017.03.016
Thomas M Stubbs, Marc Jan Bonder, Anne-Katrien Stark, Felix Krueger, Ferdinand von Meyenn, Oliver Stegle, Wolf Reik, None, Multi-tissue DNA methylation age predictor in mouse Genome Biology. ,vol. 18, pp. 68- 68 ,(2017) , 10.1186/S13059-017-1203-5