| Population specific biomarkers of human aging: a big data study using South Korean, Canadian, and Eastern European patient populations P Mamoshina, K Kochetov, E Putin, F Cortese, A Aliper, WS Lee, SM Ahn, ... The Journals of Gerontology: Series A 73 (11), 1482-1490, 2018 | 177 | 2018 |
| Noise masking recurrent neural network for respiratory sound classification K Kochetov, E Putin, M Balashov, A Filchenkov, A Shalyto Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 95 | 2018 |
| Blood biochemistry analysis to detect smoking status and quantify accelerated aging in smokers P Mamoshina, K Kochetov, F Cortese, A Kovalchuk, A Aliper, E Putin, ... Scientific reports 9 (1), 142, 2019 | 89 | 2019 |
| DeepMAge: a methylation aging clock developed with deep learning F Galkin, P Mamoshina, K Kochetov, D Sidorenko, A Zhavoronkov Aging and disease 12 (5), 1252, 2021 | 86 | 2021 |
| Wheeze detection using convolutional neural networks K Kochetov, E Putin, S Azizov, I Skorobogatov, A Filchenkov Progress in Artificial Intelligence: 18th EPIA Conference on Artificial …, 2017 | 25 | 2017 |
| Psychological factors substantially contribute to biological aging: evidence from the aging rate in Chinese older adults F Galkin, K Kochetov, D Koldasbayeva, M Faria, HH Fung, AX Chen, ... Aging (Albany NY) 14 (18), 7206, 2022 | 24 | 2022 |
| PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence A Zhavoronkov, K Kochetov, P Diamandis, M Mitina Aging (Albany NY) 12 (23), 23548, 2020 | 23 | 2020 |
| ACM international conference proceeding series B Li, H Yin, C Wang, YN Li, Y Hu, P Ye, L Yang, J Li, W Lu, Y Chen, ... Association for Computing Machinery, 2020 | 21 | 2020 |
| Blood biochemistry analysis to detect smoking status and quantify accelerated aging in smokers. Sci Rep. 2019; 9: 142 P Mamoshina, K Kochetov, F Cortese, A Kovalchuk, A Aliper, E Putin, ... | 11 | |
| Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability F Galkin, K Kochetov, M Keller, A Zhavoronkov, N Etcoff Aging (Albany NY) 14 (12), 4935, 2022 | 10 | 2022 |
| Generative adversarial networks for respiratory sound augmentation K Kochetov, A Filchenkov Proceedings of the 2020 1st International Conference on Control, Robotics …, 2020 | 9 | 2020 |
| Methylation data signatures of aging and methods of determining a methylation aging clock F Galkin, KS Kochetov, P Mamoshina, A Zavoronkovs US Patent App. 17/479,892, 2022 | 6 | 2022 |
| Testing for batch effect through age predictors P Mamoshina, K Kochetov, E Putin, A Aliper, A Zhavoronkov bioRxiv, 531863, 2019 | 6 | 2019 |
| DeepMAge: a methylation aging clock developed with deep learning. Aging Dis 12: 1252–1262 F Galkin, P Mamoshina, K Kochetov, D Sidorenko, A Zhavoronkov | 5 | 2020 |
| Adapting Blood DNA Methylation Aging Clocks for Use in Saliva Samples With Cell-type Deconvolution F Galkin, K Kochetov, P Mamoshina, A Zhavoronkov Frontiers in Aging 2, 697254, 2021 | 4 | 2021 |
| Adversarial autoencoder architecture for methods of graph to sequence models A Zavoronkovs, EO Putin, KS Kochetov US Patent App. 17/800,129, 2023 | 2 | 2023 |
| Identification of smokingstatus from routine blood test results using deep neural network analysis. N Skjodt, P Mamoshina, K Kochetov, F Cortese, A Kovalchuk, A Aliper, ... European Respiratory Journal 52 (suppl 62), 2018 | 1 | 2018 |
