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| Issue | Title | |
| Vol 14, No 4 (2021) | Machine learning based on laboratory data for disease prediction | Abstract similar documents |
| A. V. Gusev, R. E. Novitskiy, A. A. Ivshin, A. A. Alekseev | ||
| "... гг. (по группам ключевых словосочетаний “machine learning”, “laboratory data”, “clinical events”, “prediction ..." | ||
| Vol 14, No 3 (2021) | COVID-19 pandemic prediction model based on machine learning in selected regions of the Russian Federation | Abstract similar documents |
| D. V. Gavrilov, R. V. Abramov, А. V. Kirilkina, А. А. Ivshin, R. E. Novitskiy | ||
| "... от 2,3% до 24% для 85 регионов России. Показано преимущество метода машинного обучения CatBoost перед линейной ..." | ||
| Vol 17, No 4 (2024) | Scenario modeling of the drug prescription process for children: application of machine learning methods | Abstract similar documents |
| А. А. Kondrashov, М. М. Kurashov, Е. Е. Loskutova | ||
| "... Цель: определение наиболее подходящего метода машинного обучения для решения задачи назначения ..." | ||
| Online First | Differential chemoproteomic analysis of RRS-1 candidate molecule and molecules of several nonsteroidal anti-inflammatory drugs | Abstract similar documents |
| P. A. Galenko-Yaroshevsky, I. Yu. Torshin, A. N. Gromov, O. A. Gromova, K. F. Suzdalev, R. A. Murashko, A. V. Zelenskaya, A. V. Zadorozhniy, T. R. Glechyan, G. V. Simavonyan, E. M.I. Muhammad | ||
| "... , nimesulide, ketorolac) on human proteome was carried out on the basis of numerical prediction algorithms over ..." | ||
| Vol 17, No 3 (2024) | Differential chemoproteomic analysis of RRS-1 candidate molecule and molecules of several nonsteroidal anti-inflammatory drugs | Abstract similar documents |
| P. A. Galenko-Yaroshevsky, I. Yu. Torshin, A. N. Gromov, O. A. Gromova, K. F. Suzdalev, R. A. Murashko, A. V. Zelenskaya, A. V. Zadorozhniy, T. R. Glechyan, G. V. Simavonyan, E.M. I. Muhammad | ||
| "... ) on human proteome was carried out on the basis of numerical prediction algorithms over the space ..." | ||
| Vol 18, No 4 (2025) | Artificial intelligence in dermatology: a comparative analysis of computer vision programs based on machine learning models | Abstract similar documents |
| D. I. Korabelnikov, A. I. Lamotkin | ||
| Vol 18, No 1 (2025) | Problems of using phenol (hydroxybenzene) and parabens as pharmaceutical stabilizers: analysis using machine learning methods | Abstract similar documents |
| I. Yu. Torshin, O. A. Gromova | ||
| Vol 18, No 3 (2025) | Chemoproteomic analysis of tenoxicam compared with other nonsteroidal anti-inflammatory drugs | Abstract similar documents |
| I. Yu. Torshin, A. N. Gromov, O. A. Gromova | ||
| Vol 13, No 2 (2020) | Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomenon of “fake news | Abstract similar documents |
| I. Yu. Torshin, O. A. Gromova, L. V. Stakhovskaya, N. P. Vanchakova, A. N. Galustyan, Zh. D. Kobalava, T. R. Grishina, A. N. Gromov, I. A. Ilovaiskaya, V. M. Kodentsova, A. G. Kalacheva, O. A. Limanova, V. A. Maksimov, S. I. Malyavskaya, E. V. Mozgovaya, N. I. Tapilskaya, K. V. Rudakov, V. A. Semenov | ||
| Vol 17, No 4 (2024) | Chemoreactome prediction of anti-inflammatory, analgesic, ulcerogenic effects of the candidate molecule N-allylimidazole-zinc in comparison with zinc derivatives of nonsteroidal anti-inflammatory drugs | Abstract similar documents |
| P. A. Galenko-Yaroshevsky, A. V. Sergeeva, I. Yu. Torshin, A. N. Gromov, I. A. Reyer, O. A. Gromova, B. A. Trofimov, L. N. Parshina, R. A. Murashko, A. V. Zadorozhniy, A. V. Zelenskaya, N. S. Sergeev, Yu. V. Tovkach, O. N. Gulevskaya, I. V. Sholl | ||
| "... методологии (новейшего направления приложения систем машинного обучения в области постгеномной фармакологии ..." | ||
| Vol 14, No 2 (2021) | Artificial intelligence in healthcare: possibilities of patent protection | Abstract similar documents |
| T. N. Erivantseva, Yu. V. Blokhina | ||
| "... интеллекта (ИИ) и машинного обучения (МО) в медицине. На основании анализа научных публикаций определены ..." | ||
| Vol 17, No 1 (2024) | Chemoreactomic analysis of acyzole in comparison with zinc derivatives of nonsteroidal anti-inflammatory drugs | Abstract similar documents |
| P. A. Galenko-Yaroshevsky, I. Yu. Torshin, A. N. Gromov, O. A. Gromova, R. A. Murashko, A. V. Zelenskaya, L. O. Alukhanyan, O. V. Shelemekh | ||
| "... свойства известны. Процедура анализа основана на новейших технологиях машинного обучения, разрабатываемых ..." | ||
| Vol 17, No 2 (2024) | Chemoreactomic study of fonturacetam effects: molecular mechanisms of influence on adipose tissue metabolism | Abstract similar documents |
| O. A. Gromova, I. Yu. Torshin | ||
| "... на хемореактомной методологии – новейшем направлении приложения систем машинного обучения в области постгеномной ..." | ||
| Vol 17, No 1 (2024) | Chemoreactome screening of aquacobalamin and heptamethyl ester of cyanoaquacobyrinic acid cytotoxic effects on tumor cells with experimental confirmation on BT-474 and A549 cell | Abstract similar documents |
| I. Yu. Torshin, M. V. Filimonova, O. A. Gromova, L. A. Maiorova, M. A. Sorokina, D. E. Frolova, A. N. Gromov, I. A. Reyer | ||
| "... изоморфизма хемографов, которая является расширением алгебраического подхода к задачам машинного обучения ..." | ||
| 1 - 14 of 14 Items | ||
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