Genetic Prediction of Cancer Recurrence: Scientists Verify Reliability of Computer Models
In biomedical research, machine learning algorithms are often used to analyse data—for instance, to predict cancer recurrence. However, it is not always clear whether these algorithms are detecting meaningful patterns or merely fitting random noise in the data. Scientists from HSE University, IBCh RAS, and Moscow State University have developed a test that makes it possible to determine this distinction. It could become an important tool for verifying the reliability of algorithms in medicine and biology. The study has been published on arXiv.
Machine learning methods help analyse complex biological data, ie for predicting the likelihood of cancer recurrence based on gene expression, which reflects the activity levels of specific DNA regions within cells. However, it is not always clear whether these algorithms are detecting meaningful patterns or merely fitting random noise in the data.
A team of scientists from HSE University, IBCh RAS, and Moscow State University has developed a test to assess how reliably the classifier distinguishes between different patient groups. In this case, the two groups were patients who experienced a recurrence of the disease and those who did not. A model performs correctly if it effectively captures biologically meaningful differences. If the algorithm simply separates the data at random, its accuracy may appear deceptively high. The researchers focused on linear classifiers, one of the most widely used ML tools in biomedicine.
Anton Zhiyanov
'We aimed to test whether randomly generated (synthetic) data could be separated by a linear classifier as effectively as real biological samples. To do this, we calculated an upper bound on the p-value, which indicates the likelihood that the model is merely "guessing." The lower this p-value, the more reliable the classifier,' explains Anton Zhiyanov, Research Fellow at the HSE Laboratory of Molecular Physiology.
The researchers conducted a series of experiments using synthetic data, allowing them to precisely control the degree of differences between classes. They then applied the new test to real-world medical models that predict the risk of breast cancer recurrence.
The results showed that most classifiers failed to capture any meaningful differences between patients with and without recurrence. Further analysis revealed that 559 out of 570 models produced results consistent with random chance. This suggests that many algorithms may appear accurate, while in reality their predictions are driven by coincidences rather than genuine patterns.
However, the researchers also identified reliable models that reveal biologically meaningful patterns. One such model was a classifier that focused on the activity levels of the ELOVL5 and IGFBP6 genes. This algorithm was further tested on an independent data sample, confirming that differences in the expression of these genes are indeed linked to the risk of cancer recurrence.
Each point on the graph represents a patient, with the expression levels of two genes measured: IGFBP6 on the X-axis and ELOVL5 on the Y-axis. The orange dots represent patients with a recurrence, while the blue dots represent those without. In the first graph, these points (patients) are clearly separated by a straight line, representing a linear classifier. In the second graph, the points are randomly distributed, and the classifier fails to identify any patterns between gene expression and actual recurrence.
Alexander Tonevitsky
'Our test could become an important tool for verifying the reliability of algorithms in biology and medicine. It helps prevent false conclusions and emphasises models that truly identify important patterns, which is crucial for making decisions about patient treatment,' comments Alexander Tonevitsky, Professor at the HSE Faculty of Biology and Biotechnology.
The study was conducted with support from HSE University's Basic Research Programme within the framework of the Centres of Excellence project.
See also:
Scientists Develop Effective Microlasers as Small as a Speck of Dust
Researchers at HSE University–St Petersburg have discovered a way to create effective microlasers with diameters as small as 5 to 8 micrometres. They operate at room temperature, require no cooling, and can be integrated into microchips. The scientists relied on the whispering gallery effect to trap light and used buffer layers to reduce energy leakage and stress. This approach holds promise for integrating lasers into microchips, sensors, and quantum technologies. The study has been published in Technical Physics Letters.
HSE Scientists Test New Method to Investigate Mechanisms of New Word Acquisition
Researchers at the HSE Centre for Language and Brain were among the first to use transcranial alternating current stimulation to investigate whether it can influence the acquisition of new words. Although the authors of the experiment have not yet found a link between brain stimulation and word acquisition, they believe that adjusting the stimulation parameters may yield different results in the future. The study has been published in Language, Cognition and Neuroscience.
Twenty vs Ten: HSE Researcher Examines Origins of Numeral System in Lezgic Languages
It is commonly believed that the Lezgic languages spoken in Dagestan and Azerbaijan originally used a vigesimal numeral system, with the decimal system emerging later. However, a recent analysis of numerals in various dialects, conducted by linguist Maksim Melenchenko from HSE University, suggests that the opposite may be true: the decimal system was used originally, with the vigesimal system developing later. The study has been published in Folia Linguistica.
Scientists Rank Russian Regions by Climate Risk Levels
Researchers from HSE University and the Russian Academy of Sciences have assessed the levels of climate risks across Russian regions. Using five key climate risks—heatwaves, water stress, wildfires, extreme precipitation, and permafrost degradation—the scientists ranked the country’s regions according to their need for adaptation to climate change. Krasnoyarsk Krai, Irkutsk Region, and Sverdlovsk Region rank among the highest for four of the five climate risks considered. The study has been published in Science of the Total Environment.
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.