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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Tailor: A Nonparametric and Rapid Score Calibration Method for Database Search-Based Peptide Identification in Shotgun Proteomics

Journal of Proteome Research. 2020. No. 19(4). P. 1481–1490.
Sulimov P., Kertesz-Farkas A.

Peptide-spectrum-match (PSM) scores used in database searching are calibrated to spectrum- or spectrum-peptide-specific null distributions. Some calibration methods rely on specific assumptions and use analytical models (e.g., binomial distributions), whereas other methods utilize exact empirical null distributions. The former may be inaccurate because of unjustified assumptions, while the latter are accurate, albeit computationally exhaustive. Here, we introduce a novel, nonparametric, heuristic PSM score calibration method, called Tailor, which calibrates PSM scores by dividing them with the top 100-quantile of the empirical, spectrum-specific null distributions (i.e., the score with an associated p-value of 0.01 at the tail, hence the name) observed during database searching. Tailor does not require any optimization steps or long calculations; it does not rely on any assumptions on the form of the score distribution (i.e., if it is, e.g., binomial); however, it relies on our empirical observation that the mean and the variance of the null distributions are correlated. In our benchmark, we re-calibrated the match scores of XCorr from Crux, HyperScore scores from X!Tandem, and the p-values from OMSSA with the Tailor method and obtained more spectrum annotations than with raw scores at any false discovery rate level. Moreover, Tailor provided slightly more annotations than E-values of X!Tandem and OMSSA and approached the performance of the computationally exhaustive exact p-value method for XCorr on spectrum data sets containing low-resolution fragmentation information (MS2) around 20-150 times faster. On high-resolution MS2 data sets, the Tailor method with XCorr achieved state-of-the-art performance and produced more annotations than the well-calibrated residue-evidence (Res-ev) score around 50-80 times faster.

Research target: Biology Computer Science
Priority areas: IT and mathematics
Language: English
DOI
Text on another site
Keywords: Fast algorithmsheuristicspectrum identificationDatabase searchpeptide assignmentscore calibrationPSM scores
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