Zobrazeno 1 - 10
of 16
pro vyhledávání: '"A. S. Polonskaia"'
Publikováno v:
Опухоли головы и шеи, Vol 11, Iss 4, Pp 97-109 (2022)
Epidermal growth factor receptor inhibitors (EGFR) have a high rate of class-specific dermatologic adverse events. Supportive treatment of dermatologic adverse events decreases their severity, minimizes the need for dose de-escalation / discontinuati
Externí odkaz:
https://doaj.org/article/52b323e81ad84360acc788aa1e5428a4
Publikováno v:
Медицинский совет, Vol 0, Iss 20, Pp 157-164 (2020)
Introduction. Dermatologic adverse events (DAEs) occur in 50-90% of cases during anti-EGFR monoclonal antibody treatment. Positive correlation between the severity of acneiform rash (AR) and the effectiveness of anti-EGFR management is established. L
Externí odkaz:
https://doaj.org/article/c198a3dfc9394600ada084952a7bc1e4
Autor:
Aleksandra S. Polonskaia, Anna V. Michenko, Larisa S. Kruglova, Evgeniya A. Shatokhina, Andrey N. Lvov
Publikováno v:
Consilium Medicum, Vol 25, Iss 6, Pp 400-405 (2023)
Modern antitumor therapy includes novel targeted and immunotherapeutic options specifically targeting tumor targets. However, many of these targets are also expressed in the constantly proliferating epidermis of the skin, leading to derangement of pr
Externí odkaz:
https://doaj.org/article/74b86a386ced4439a74027ce9cb9cc87
Autor:
E M Seredenina, A A Kamalov, O. A. Georginova, A. G. Plisyuk, E. A. Shatokhina, T N Krasnova, Ya A Orlova, E P Pavlikova, Valentin Sinitsyn, A. S. Polonskaia, L. S. Kruglova, Elena Mershina
Publikováno v:
Immunologiya. 42:243-253
Coronavirus infection COVID-19 is an acute respiratory viral disease caused by a novel beta-coronavirus SARS-CoV-2. In 81 % of cases, mortality in COVID-19 patients is associated with the development of acute respiratory distress syndrome (ARDS). Ano
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::77d814ad8c31b3683f10799d0c8854b5
https://doi.org/10.33029/0206-4952-2021-42-3-243-253
https://doi.org/10.33029/0206-4952-2021-42-3-243-253
Autor:
Irina V. Barabanova, Iana S. Polonskaia, Pavel Vychuzhanin, Nikolay O. Nikitin, Anna V. Kalyuzhnaya
Publikováno v:
Procedia Computer Science. 178:414-423
In this paper, we propose an evolutionary learning approach for flexible identification of custom composite models for classification problems. To solve this problem in an efficient way, the problem-specific evolutionary operators are proposed and th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3fa4def36c08fec72aa5fd7e2573fe85
https://doi.org/10.1016/j.procs.2020.11.043
https://doi.org/10.1016/j.procs.2020.11.043
Publikováno v:
Developments in Maritime Technology and Engineering ISBN: 9781003216599
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::649a5601bc6b4be9ad2372def9ebcd04
https://doi.org/10.1201/9781003216599-82
https://doi.org/10.1201/9781003216599-82
Autor:
Iana S. Polonskaia, Anna V. Kalyuzhnaya, Alexander Hvatov, Nikolay O. Nikitin, Xiaohao Wang, Georgii V. Grigorev, Xiang Qian
Publikováno v:
GECCO Companion
In this paper, we propose the evolutionary approach for the generative design of microfluidic channel geometry. Sets of candidate solutions for geometry of single cell analysis devices can be used to simplify the decision-making process for micro-dev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0aa879b45c089314ecfcd6d263e2630c
https://doi.org/10.1145/3449726.3462740
https://doi.org/10.1145/3449726.3462740
Publikováno v:
CEC
In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogeneous pipelines that consist of different blocks: machine learning model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd76f1d4fa15722e6ad25e5166b0fec5
https://doi.org/10.1109/cec45853.2021.9504773
https://doi.org/10.1109/cec45853.2021.9504773
Autor:
Pavel Vychuzhanin, Alexander V. Boukhanovsky, Anna V. Kalyuzhnaya, Iana S. Polonskaia, Ilia Revin, Gleb Maximov, Nikolay O. Nikitin, Mikhail Sarafanov, Irina V. Barabanova
The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53f1382bdc4d736b63a1ae7ced640229
Autor:
Eugene Semenkin, Viktor Kessler, Iana S. Polonskaia, Alina Skorokhod, Danila Mamontov, Friedhelm Schwenker
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030209834
MPRSS
MPRSS
In this paper we present a study on multi-modal pain intensity recognition based on video and bio-physiological sensor data. The newly recorded SenseEmotion dataset consisting of 40 individuals, each subjected to three gradually increasing levels of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c101becb451fa0f4d999bc96f783a25
https://doi.org/10.1007/978-3-030-20984-1_8
https://doi.org/10.1007/978-3-030-20984-1_8