• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Machine Learning Links Two New Genes to Ischemic Stroke

Machine Learning Links Two New Genes to Ischemic Stroke

© iStock

A team of scientists from HSE University and the Kurchatov Institute used machine learning methods to investigate genetic predisposition to stroke. Their analysis of the genomes of over 5,000 people identified 131 genes linked to the risk of ischemic stroke. For two of these genes, the association was found for the first time. The paper has been published in PeerJ Computer Science.

Ischemic stroke is a major cause of death and disability worldwide. This condition occurs when blood supply to a part of the brain is interrupted, causing cell death and impaired brain function. Scientists have long studied the genetic factors influencing stroke risk, but a definitive list of genes linked to stroke predisposition has yet to be established. There are hopes that artificial intelligence methods may provide answers in this regard.

A team of scientists from the HSE Faculty of Computer Science and the Kurchatov Institute proposed using machine learning algorithms to analyse genetic predisposition to stroke. They analysed genomic data from 5,500 unrelated individuals over the age of 55, including ischemic stroke survivors and their healthy counterparts. Samples for the study were collected from 11 laboratories in Europe and 13 in the United States.

The analysis was based on the concept of ranking through learning. First, the researchers developed a predictive model in which the key parameter was the presence or absence of a stroke. Single nucleotide polymorphisms (SNPs), which are variations in the genome at specific sites, were used as markers. The scientists then ranked these markers and selected the most significant ones.

SNPs were analysed and selected using various methods, enabling a new analysis of the data and the identification of genes previously not associated with ischemic stroke. The list of 'suspicious' genetic markers common to two or more methods highlights the reliability of the results.

Working with such a large dataset—nearly 900,000 SNPs per 5,500 participants—required us to move beyond purely statistical analysis methods. Machine learning made it possible to process all of this. As a result, we identified 131 genes, most of which had already been linked to ischemic stroke. However, for two of these genes, this was the first time we discovered the association,' explains Dmitry Ignatov, Head of the Laboratory for Models and Methods of Computational Pragmatics at HSE University.

In particular, the scientists found an association between stroke and ACOT11, a gene involved in fatty acid metabolism and shown in animal experiments to affect inflammatory processes and blood lipid levels. The second gene newly linked to ischemic stroke is UBQLN1, which is involved in the mechanisms that protect cells from oxidative stress. There is evidence that a mutation in this gene is associated with neurodegenerative diseases.

These discoveries could help develop multigenic risk models that predict a person's predisposition to stroke. Information about the newly associated genes could also serve as the foundation for developing drugs and therapies aimed at reducing the risk of ischemic stroke.

Gennady Khvorykh

'Identifying two new stroke-associated genes is an excellent outcome for any method. Our machine learning approach clearly holds strong potential for detecting genes linked to diseases that result from a variety of factors,' comments Gennady Khvorykh, Chief Specialist at the Kurchatov Institute.

The proposed approach to analysing genetic markers demonstrates versatility and can be effectively adapted for a wide range of studies beyond ischemic stroke. This methodology can be applied to any diseases or markers with data available in the 'sample—SNP—class' format.

'Although we initially developed this tool for a specific task, the results reveal its potential in a broader context. The ability to work with a variety of genetic data makes our method valuable to researchers across various fields of biology and medicine,' says Stefan Nikolić, graduate of the Faculty of Computer Science and the Doctoral School of Computer Science at HSE University.

See also:

HSE Researchers Teach Neural Network to Distinguish Origins from Genetically Similar Populations

Researchers from the AI and Digital Science Institute, HSE Faculty of Computer Science, have proposed a new approach based on advanced machine learning techniques to determine a person’s genetic origin with high accuracy. This method uses graph neural networks, which make it possible to distinguish even very closely related populations.

HSE Economists Reveal the Secret to Strong Families

Researchers from the HSE Faculty of Economic Sciences have examined the key factors behind lasting marriages. The findings show that having children is the primary factor contributing to marital stability, while for couples without children, a greater income gap between spouses is associated with a stronger union. This is the conclusion reported in Applied Econometrics.

Fifteen Minutes on Foot: How Post-Soviet Cities Manage Access to Essential Services

Researchers from HSE University and the Institute of Geography of the Russian Academy of Sciences analysed three major Russian cities to assess their alignment with the '15-minute city' concept—an urban design that ensures residents can easily access essential services and facilities within walking distance. Naberezhnye Chelny, where most residents live in Soviet-era microdistricts, demonstrated the highest levels of accessibility. In Krasnodar, fewer than half of residents can easily reach essential facilities on foot, and in Saratov, just over a third can. The article has been published in Regional Research of Russia.

HSE Researchers Find Counter-Strike Skins Outperform Bitcoin and Gold as Alternative Investments

Virtual knives, custom-painted machine guns, and gloves are common collectible items in videogames. A new study by scientists from HSE University suggests that digital skins from the popular video game Counter-Strike: Global Offensive (CS:GO) rank among the most profitable types of alternative investments, with average annual returns exceeding 40%. The study has been published in the Social Science Research Network (SSRN), a free-access online repository.

HSE Neurolinguists Reveal What Makes Apps Effective for Aphasia Rehabilitation

Scientists at the HSE Centre for Language and Brain have identified key factors that increase the effectiveness of mobile and computer-based applications for aphasia rehabilitation. These key factors include automated feedback, a variety of tasks within the application, extended treatment duration, and ongoing interaction between the user and the clinician. The article has been published in NeuroRehabilitation.

'Our Goal Is Not to Determine Which Version Is Correct but to Explore the Variability'

The International Linguistic Convergence Laboratory at the HSE Faculty of Humanities studies the processes of convergence among languages spoken in regions with mixed, multiethnic populations. Research conducted by linguists at HSE University contributes to understanding the history of language development and explores how languages are perceived and used in multilingual environments. George Moroz, head of the laboratory, shares more details in an interview with the HSE News Service.

Slim vs Fat: Overweight Russians Earn Less

Overweight Russians tend to earn significantly less than their slimmer counterparts, with a 10% increase in body mass index (BMI) associated with a 9% decrease in wages. These are the findings made by Anastasiia Deeva, lecturer at the HSE Faculty of Economic Sciences and intern researcher in Laboratory of Economic Research in Public Sector. The article has been published in Voprosy Statistiki.

Scientists Reveal Cognitive Mechanisms Involved in Bipolar Disorder

An international team of researchers including scientists from HSE University has experimentally demonstrated that individuals with bipolar disorder tend to perceive the world as more volatile than it actually is, which often leads them to make irrational decisions. The scientists suggest that their findings could lead to the development of more accurate methods for diagnosing and treating bipolar disorder in the future. The article has been published in Translational Psychiatry.

Scientists Develop AI Tool for Designing Novel Materials

An international team of scientists, including researchers from HSE University, has developed a new generative model called the Wyckoff Transformer (WyFormer) for creating symmetrical crystal structures. The neural network will make it possible to design materials with specified properties for use in semiconductors, solar panels, medical devices, and other high-tech applications. The scientists will present their work at ICML, a leading international conference on machine learning, on July 15 in Vancouver. A preprint of the paper is available on arxiv.org, with the code and data released under an open-source license.

HSE Linguists Study How Bilinguals Use Phrases with Numerals in Russian

Researchers at HSE University analysed over 4,000 examples of Russian spoken by bilinguals for whom Russian is a second language, collected from seven regions of Russia. They found that most non-standard numeral constructions are influenced not only by the speakers’ native languages but also by how frequently these expressions occur in everyday speech. For example, common phrases like 'two hours' or 'five kilometres’ almost always match the standard literary form, while less familiar expressions—especially those involving the numerals two to four or collective forms like dvoe and troe (used for referring to people)—often differ from the norm. The study has been published in Journal of Bilingualism.