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News
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

NY : Association for Computing Machinery (ACM), 2021.
Academic editor: J. Hongmei, H. Xiuzhen, Z. Jiajie

Continuing the annual tradition, the conference focuses on interdisciplinary research linking computer science, mathematics, statistics, biology, bioinformatics, biomedical informatics, and health informatics.

Chapters
FARM: hierarchical association rule mining and visualization method
Petr T., Oleg S., Lukashina N. et al., , in: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics.: NY: Association for Computing Machinery (ACM), 2021.
Associations search is one of the methods of data analysis. Association Rule Mining (ARM) approach can construct association rules from observational data, but the most widely used algorithm Apriori typically produces large number of unstructured results without any ranking or statistical significance. We propose a novel method for association rules mining FARM (Fishbone Association Rule ...
Added: December 7, 2021
Research target: Computer Science
Language: English
Text on another site
Keywords: Computational biologybioinformatics
Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
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The matrix profile (MP) quickly became one of the most important time series preprocessing methods when it was introduced in 2016, facilitating the solution of a wide range of time series analysis problems, in particular anomaly and pattern detection problems. The high significance has led to the emergence of various tools for MP calculating, but ...
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Under conditions of dynamic socio-economic changes, effective planning and decision-making are key factors in ensuring a high quality of life for the population in territories. This paper presents a decision support system for territorial administrations for the development and implementation of sustainable development strategies, using one of the regions of the Russian Federation – the ...
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Новые информационные технологии в исследовании сложных структур. Материалы шестнадцатой международной конференции 21–25 Сентября 2026 г.
Томск: Издательство Томского государственного университета, 2026.
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Added: September 30, 2026
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Accurate predictions and large-scale identification of protein-protein interactions (PPIs) are crucial for understanding their inherent biological mechanisms and protein functions in virtually all biological processes. Nowadays, graph-based deep learning models have made significant contributions in modeling proteins with physicochemical and geometric features. However, most of these models rely on conventional graph construction methods, such as ...
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Added: December 11, 2024
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