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News
October 5, 2026
‘The Climate Transition Is Not Necessarily a Limitation for Business
Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
October 5, 2026
Africa, Youth, and Civic Dialogue: Public Diplomacy Discussed at HSE University
In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.
October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.

 

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?

Полигенная оценка риска с помощью моделей глубинного обучения

.
Perelygin V., Poptsova M., Камелин А. В., Ракитько А. С.
Language: Russian
Full text
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Keywords: Машинное обучение в БиоинформатикеБиоинформатика и медицина

In book

Proceedings of 11th Moscow Conference on Computational Molecular Biology MCCMB'23
IITP RAS, 2023.
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Pericyte-derived cancer-associated fibroblasts correlate with poor survival and are enriched after chemoradiotherapy in glioblastoma
Aly Ismailov, Poptsova M., Plos One 2026 Vol. 21 No. 9 Article e0355902
Added: October 2, 2026
GENA-LM: a family of open-source foundational DNA language models for long sequences
Fishman V., Kuratov Y., Shmelev A. et al., Nucleic Acids Research 2025 Vol. 53 No. 2 Article gkae1310
Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To ...
Added: August 1, 2025
Параллельные вычислительные технологии – XVII всероссийская конференция с международным участием, ПаВТ'2023, г. Санкт-Петербург, 28–30 марта 2023 г. Короткие статьи и описания плакатов
Издательский центр ЮУрГУ, 2023.
Данный сборник содержит статьи, включенные в программу всероссийской научной конференции с международным участием «Параллельные вычислительные технологии 2023». Конференция проходила 28–30 марта 2023 года в Университете ИТМО (г. СанктПетербург). Подробную информацию о конференции можно найти в сети Интернет по адресу http://agora.guru.ru/pavt. Отпечатано с авторских оригиналов. ...
Added: November 27, 2023
Randomness in Cancer Breakpoint Prediction
Cheloshkina K., Bzhikhatlov I., Poptsova M., Journal of Computational Biology 2021 Vol. 28 No. 7 P. 716–731
Cancer genomes are susceptible to multiple rearrangements by deleting, inserting, and translocating genomic regions. Recently, the problem of finding determinants of breakpoint formations was approached with machine learning methods; however, unlike cancer point mutations, breakpoint prediction appeared to be a more difficult task, and various machine learning models did not achieve high prediction power often ...
Added: September 10, 2021
ColocML: machine learning quantifies co-localization between mass spectrometry images
Ovchinnikova K., Lachlan S., Rakhlin A. et al., Bioinformatics 2020 P. 1–10
Motivation Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. Results We ...
Added: March 15, 2020
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