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:
Team Success: Aligning Means with Objectives
In corporations, sports, and academia, people often face challenges they cannot handle alone. In such cases, selecting the right team is crucial. Tatiana Mayskaya, Associate Professor at the HSE Faculty of Economic Sciences and the International College of Economics and Finance, together with colleagues from foreign universities, examined team characteristics and found that less diverse teams are better suited to objectives where a high average performance is important, whereas more diverse teams are preferable when avoiding failure is critical. The paper has been published in Economic Theory.
Economists Propose More Effective Approach to Reducing Smoking
Economists at HSE University have examined how smokers respond to changes in cigarette prices. When tobacco prices increase, cigarette consumption does not always decline. In fact, spending on tobacco may even rise: according to the researchers, a 1% decrease in cigarette affordability leads to a 0.28% increase in per capita tobacco expenditure. The findings suggest that to reduce smoking rates, tobacco prices must rise faster than household incomes. The study has been published in Voprosy Statistiki.
Biologists Discover Unique Properties of MiR-93-5p MicroRNA in Prostate Cancer
Researchers at the International Laboratory of Microphysiological Systems of the HSE Faculty of Biology and Biotechnology investigated how different isoforms of the same microRNA influence gene function in prostate adenocarcinoma. The study found that in some cases, microRNAs can reinforce each other’s effects by targeting and suppressing the same genes. This finding offers a fresh perspective on the molecular mechanisms underlying tumour development and on the search for disease biomarkers. The results have been published in PeerJ.
HSE Researchers Provide the World’s First Legal Definition of a Digital Ecosystem
Digital ecosystems have evolved from a technological innovation into a fundamental institution of the modern economy over the past few years. According to HSE University’s latest estimates, they account for 8.5% of Russia’s GDP. Previously, no jurisdiction had a statutory definition of what constitutes a digital ecosystem. HSE University researchers have addressed this gap by proposing the first legal concept of a digital ecosystem. Their article, ‘The Digital Ecosystem as a Novel Economic Phenomenon and Legal Concept,’ has been published in the BRICS Law Journal.
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
When Looking at Their Own Faces, Men Forget Everything
In an experiment involving 15 healthy men, scientists at HSE University investigated how different phases of the cardiac cycle influence the excitability of the motor cortex when participants viewed either their own photograph or the faces of strangers. The researchers found that when participants looked at their own image, the brain’s response to signals from the heart was weaker, meaning that the influence of cardiac activity on the motor cortex decreased. This finding came contrary to expectations, as self-focused attention was thought to enhance the brain's sensitivity to internal bodily signals. The study has been published in Frontiers in Signal Processing.
HSE Researchers Discover Who Eats Out in Russia—And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
Scientists Model How Interactions Between Societies Can Trigger Chaotic Behaviour
Scientists at HSE MIEM have proposed a mathematical model explaining how interactions between societies can influence their stability. Based on the classical theory of evolutionary games, the study reveals an unexpected effect: even a weak informational influence of one society on another can cause one society to remain stable while the other exhibits chaotic behaviour among its individual members. The study has been published in the International Journal of Bifurcation and Chaos.
Ancient Craniiform Brachiopod: A Newly Discovered Species with a Unique Shell Shape and Lifestyle
Scientists from HSE University, MSU, and Tallinn University of Technology have studied a fossil species of ancient brachiopods that lived in a warm sea in what is now northern Estonia more than 445 million years ago. These ancient brachiopods developed a cup-shaped shell with a protective 'cap' that shielded them from overgrowth by other marine organisms. The study has been published in Palaeogeography, Palaeoclimatology, Palaeoecology.
Scientists Develop Bacterium-Sized Microlaser
An international team of researchers, including scientists from HSE University–St Petersburg, has developed microlasers that emit deep-ultraviolet light at a wavelength of 255 nanometres. The devices operate at room temperature, and the smallest of them measures just two micrometres in diameter—roughly the size of a bacterium. These microlasers could be used in sensors, spectroscopic systems, photonic chips, and communication devices. The paper has been published in Optics & Laser Technology.