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News
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine

Madrid : IEEE, 2018.
Academic editor: H. (. Zheng, Z. Callejas, D. Griol, H. Wang, X. Hu, H. Schmidt, J. Baumbach, J. Dickerson, L. Zhang

The IEEE BIBM 2018 promises to provide great scientific quality and to have a broad impact, with world renowned scientists as keynote speakers and invited speakers, contributed talks at a highly competitive acceptance rate, special issue publications in high-caliber scientific journals, and a broad participation of the research communities serving on the Program Committee and the organizing committees for workshops, tutorials, and posters. The scientific program highlights five themes to provide breadth, depth, and synergy for research collaboration: (1) genomics and molecular structure, function, and evolution; (2) computational systems biology; (3) medical informatics and translational bioinformatics; (4) cross-cutting computational methods and bioinformatics infrastructures, and (5) healthcare informatics, which includes approximately 20 topics.

Chapters
Recognizing Patterns of Nucleosome and DNA Structures Positioning
Tevanyan E., Poptsova M., , in: Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine.: Madrid: IEEE, 2018. P. 2808–2809.
Non-B DNA structures have a great potential to form and influence various genomic processes including transcription. One of the mechanisms of transcription regulation is nucleosome positioning. Even though only B-DNA can be wrapped around a nucleosome, non-B DNA structures can compete with a nucleosome for a genomic location. Here we used permanganate/S1 nuclease footprinting data ...
Added: February 3, 2019
Supervised Classification of Metabolic Networks
Granata I., Guarracino M., Kalyagin V. A. et al., , in: Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine.: Madrid: IEEE, 2018. P. 2688–2693.
Networks represent a convenient model for many scientific and technological problems. From power grids to biological processes and functions, from financial networks to chemical compounds, the representation of case studies with graphs enables the possibility to highlight both topological and qualitative characteristics. In this work, we are interested in the supervised classification models for data ...
Added: November 24, 2019
Language: English
Text on another site
Keywords: biomedicinebioinformatics computer science
Proceedings 2018 IEEE International Conference on Bioinformatics and Biomedicine
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This paper addresses the challenge of efficiently training Large Language Models (LLMs) on large-scale, sparse omics datasets in high-performance computing (HPC) environments. Using over 1000 BED tracks as a representative data source, we propose a method combining interval-based chunked storage, sparse matrix transformation, and parallel data loading, integrated within a PyTorch Lightning training framework. Our ...
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WWW '26: Proceedings of the ACM Web Conference 2026
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Информатика и прикладная математика: Материалы IX Международной научно-пракической конференции (31.10 - 1.11.2024 г.)
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Multimodal graph, surface, and language-based model for protein protein interaction prediction
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Accurate prediction of protein-protein interactions (PPIs) is fundamental to understanding biological processes and disease mechanisms. While deep learning offers a powerful alternative to costly experimental methods, existing approaches often overlook critical protein-surface information and rely on simplistic feature fusion techniques, thereby limiting performance. To address this, we introduce GSMFormer-PPI, a novel multimodal framework that integrates ...
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Prediction of protein-protein interactions using point transformer and spherical Convex Hull graphs
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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: November 6, 2025
Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples
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According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This ...
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Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine
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Liver metastasis is a significant factor contributing to mortality associated with colorectal cancer. Establishing the biological mechanisms of metastasis is crucial for refining diagnostics and identifying therapeutic windows for interventions. Currently, little is known of the processes that govern the development of liver metastases, the role of the tumor microenvironment, the role of epigenetics, and ...
Added: July 1, 2025
Reliable Queuing One-Way Delay Metric for Computer Networks
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The review presents the history of the creation and work of the Scientific Council “Cybernetics” (SCC)—the center for development of cybernetics in the USSR and Russia. The Council’s activity is covered as a structure that coordinates scientific and technical activity in cybernetics in the country via a system of sections: about 16 public and professional ...
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The rapid development of parallel computing technologies makes it urgent to include propaedeutics of parallel computing in the school computer science course. Since this topic is not yet included in the school curriculum, it can be done through extracurricular activities, in particular, through Internet contests. Since 2013, parallel computing tasks have become a compulsory part ...
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