| Applications of deep learning in biomedicine P Mamoshina, A Vieira, E Putin, A Zhavoronkov Molecular pharmaceutics 13 (5), 1445-1454, 2016 | 763 | 2016 |
| Deep biomarkers of human aging: application of deep neural networks to biomarker development E Putin, P Mamoshina, A Aliper, M Korzinkin, A Moskalev, A Kolosov, ... Aging (Albany NY) 8 (5), 1021, 2016 | 373 | 2016 |
| Reinforced Adversarial Neural Computer for de Novo Molecular Design E Putin, A Asadulaev, Y Ivanenkov, V Aladinskiy, B Sanchez-Lengeling, ... Journal of chemical information and modeling 58 (6), 1194-1204, 2018 | 364 | 2018 |
| Adversarial Threshold Neural Computer for Molecular de Novo Design E Putin, A Asadulaev, Q Vanhaelen, Y Ivanenkov, AV Aladinskaya, ... Molecular pharmaceutics 15 (10), 4386-4397, 2018 | 219 | 2018 |
| Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification P Mamoshina, M Volosnikova, IV Ozerov, E Putin, E Skibina, F Cortese, ... Frontiers in genetics 9, 242, 2018 | 179 | 2018 |
| 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 | 178 | 2018 |
| Human gut microbiome aging clock based on taxonomic profiling and deep learning F Galkin, P Mamoshina, A Aliper, E Putin, V Moskalev, VN Gladyshev, ... Iscience 23 (6), 101199, 2020 | 109 | 2020 |
| Noise masking recurrent neural network for respiratory sound classification K Kochetov, E Putin, M Balashov, A Filchenkov, A Shalyto International Conference on Artificial Neural Networks, 208-217, 2018 | 93 | 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), 1-10, 2019 | 87 | 2019 |
| Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects F Galkin, A Aliper, E Putin, I Kuznetsov, VN Gladyshev, A Zhavoronkov BioRxiv, 507780, 2018 | 78 | 2018 |
| In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state A Aliper, AV Belikov, A Garazha, L Jellen, A Artemov, M Suntsova, ... Aging (Albany NY) 8 (9), 2127, 2016 | 64 | 2016 |
| Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells MD West, I Labat, H Sternberg, D Larocca, I Nasonkin, KB Chapman, ... Oncotarget 9 (8), 7796, 2018 | 43 | 2018 |
| Pollen grain recognition using convolutional neural network. N Khanzhina, E Putin, A Filchenkov, E Zamyatina ESANN, 2018 | 40 | 2018 |
| Identification of novel antibacterials using machine learning techniques YA Ivanenkov, A Zhavoronkov, RS Yamidanov, IA Osterman, PV Sergiev, ... Frontiers in pharmacology, 913, 2019 | 35 | 2019 |
| A review of the biomedical innovations for healthy longevity A Moskalev, V Anisimov, A Aliper, A Artemov, K Asadullah, D Belsky, ... Aging (Albany NY) 9 (1), 7, 2017 | 33 | 2017 |
| Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes AV Artemov, E Putin, Q Vanhaelen, A Aliper, IV Ozerov, A Zhavoronkov BioRxiv, 095653, 2016 | 31 | 2016 |
| Wheeze detection using convolutional neural networks K Kochetov, E Putin, S Azizov, I Skorobogatov, A Filchenkov EPIA Conference on Artificial Intelligence, 162-173, 2017 | 25 | 2017 |
| Combating data incompetence in pollen images detection and classification for pollinosis prevention N Khanzhina, A Filchenkov, N Minaeva, L Novoselova, M Petukhov, ... Computers in biology and medicine 140, 105064, 2022 | 12 | 2022 |
| Pollen recognition for allergy and asthma management using gist features N Khanzhina, E Putin International Conference on Digital Transformation and Global Society, 515-525, 2016 | 8 | 2016 |
| COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity V Naumov, E Putin, S Pushkov, E Kozlova, K Romantsov, A Kalashnikov, ... PLoS computational biology 17 (7), e1009183, 2021 | 7 | 2021 |
