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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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Genomic analysis of human polymorphisms affecting drug-protein interactions

2017.
Pich i Rosello O., Vlasova A., Shichkova P., Markov Yuri, Vlasov P., Kondrashov F. A.
Human genetic variability is thought to account for a substantial fraction of individual biochemical characteristics — in biomedical sense, of individual drug response. However, only a handful of human genetic variants have been linked to medication outcomes. Here, we combine data on drug-protein interactions and human genome sequences to assess the impact of human variation on their binding affinity. Using data from the complexes of FDA-drugs and drug-like compounds, we predict SNPs substantially affecting the protein-ligand binding affinities. We estimate that an average individual carries ~6 SNPs affecting ~5 different FDA-approved drugs from among all of the approved compounds. SNPs affecting drug-protein binding affinity have low frequency in the population indicating that the genetic component for many ADEs may be highly personalized with each individual carrying a unique set of relevant SNPs. The reduction of ADEs, therefore, may primarily rely on the application of computational genome analysis in the clinic rather than the experimental study of common SNPs.
Research target: Earth Sciences Biology Basic Medicine Health Studies
Language: English
DOI
Text on another site
Keywords: drug design
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