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Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals
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In book
IITP RAS, 2023.
Beknazarov N., , in: Parallel Computational Technologies, 19th International Conference, PCT 2025, Moscow, Russia, April 8–10, 2025, Revised Selected Papers. (CCIS, volume 2891)Vol. 2891.: Springer, 2026. P. 3–16.
This paper addresses the challenge of efficiently training Large Language Models (LLMs) on large-scale, sparse omics datasets in high-performance computing (HPC) environments. Using over 1000 BED tracks as a representative data source, we propose a method combining interval-based chunked storage, sparse matrix transformation, and parallel data loading, integrated within a PyTorch Lightning training framework. Our ...
Added: May 19, 2026
Arteaga Moreano B. D., Chervov N., Poptsova M., Scientific Reports 2026 Vol. 16 No. 1 Article 4772
Accurate prediction of protein-protein interactions (PPIs) is fundamental to understanding biological processes and disease mechanisms. While deep learning offers a powerful alternative to costly experimental methods, existing approaches often overlook critical protein-surface information and rely on simplistic feature fusion techniques, thereby limiting performance. To address this, we introduce GSMFormer-PPI, a novel multimodal framework that integrates ...
Added: February 4, 2026
David Arteaga, Poptsova M., Computational and Structural Biotechnology Journal 2026 Vol. 31 P. 82–93
Accurate predictions and large-scale identification of protein-protein interactions (PPIs) are crucial for understanding their inherent biological mechanisms and protein functions in virtually all biological processes. Nowadays, graph-based deep learning models have made significant contributions in modeling proteins with physicochemical and geometric features. However, most of these models rely on conventional graph construction methods, such as ...
Added: December 22, 2025
D.D. Sukhoverkhova, L.N. Shchur, , in: Параллельные вычислительные технологии – XIX всероссийская конференция с международным участием, ПаВТ'2025. Короткие статьи и описания плакатов.: Издательский центр ЮУрГУ, 2025. P. 82–89.
We apply supervised deep machine learning techniques to extract properties of the anisotropic Ising model. We consider two cases of anisotropy: orthogonal and diagonal. From the predictions of the neural network, we obtained phase probability functions, from which we measured two quantities: the critical temperature and the critical exponent of the correlation length. We estimated ...
Added: December 4, 2025
Budkina A., Korneenko E., Kotov I. et al., Viruses 2021 No. 10 P. 2006
According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This ...
Added: September 19, 2025
Chertenkov V., Shchur L., Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2025 Vol. 112 No. 3 Article 034104
The main question raised in the article is whether a neural network trained on a spin lattice model in one universality class can be used to test a model in another universality class. The quantities of interest are the critical phase transition temperature and the correlation length exponent. In other words, the question of ...
Added: August 12, 2025
KUDRYAVTSEVA A., Cancers 2023
Liver metastasis is a significant factor contributing to mortality associated with colorectal cancer. Establishing the biological mechanisms of metastasis is crucial for refining diagnostics and identifying therapeutic windows for interventions. Currently, little is known of the processes that govern the development of liver metastases, the role of the tumor microenvironment, the role of epigenetics, and ...
Added: July 1, 2025
Golyadkin M., Saraev S., Makarov I., IEEE Access 2025 Vol. 13 P. 7526–7537
Manga colorization in augmented reality (AR) environments presents unique challenges, particularly when colorizing manga pages captured in photos under various real-world conditions. Testing models in AR settings for manga colorization has been a significant challenge, primarily because of the absence of suitable datasets tailored for this task. To address this, we propose a benchmark for ...
Added: April 29, 2025
Golyadkin M., Saraev S., Makarov I., , in: 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct).: IEEE, 2024. P. 608–611.
This paper introduces an innovative approach to manga colorization within augmented reality (AR) environments, focusing on the unique challenges posed by colorizing photos of manga books. We present a novel method using diffusion models to generate a synthetic dataset that accurately replicates photographed manga pages. Additionally, we have compiled a dataset of real manga photographs, ...
Added: April 29, 2025
Chertenkov V., Shchur L., Lecture Notes in Computer Science 2025 Vol. 15406 P. 434–449
Machine learning is a new tool for investigating physical models. One possible applications is the study of phase transitions analyzing the distribution of spins on regular lattices using supervised learning approach. A new question is the applicability of transfer learning, a network supervised on a particular model and used to infer information about another model.
The ...
Added: February 10, 2025
D. D. Sukhoverkhova, L. N. Shchur, Lobachevskii Journal of Mathematics 2025 Vol. 46 No. 1 P. 528–534
We investigate the possibility of extracting features of second-order phase transitions using transfer machine learning. We have performed supervised machine learning for binary classification of snapshots of the spin distribution of the isotropic Ising model. The binary classification is performed in ferromagnetic and paramagnetic phases using a known critical temperature. The trained network is used ...
Added: January 13, 2025
Gorshkov S., Ignatov D. I., Chernysheva A. et al., IEEE Access 2025 Vol. 13 P. 962–979
Identifying potentially high-performing students is crucial for universities aiming to enhance educational outcomes, for companies seeking to recruit top talents early, and for advertising platforms looking to optimize targeted marketing. This paper introduces an algorithm designed to identify students with exceptional academic performance by analyzing their subscriptions to communities on the social network VKontakte. The ...
Added: January 3, 2025
Stefan Nikolić, Ignatov D. I., Khvorykh G. et al., PeerJ Computer Science 2024 Vol. 10 Article e2454
Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model ...
Added: December 11, 2024
Sukhoverkhova D., Shchur L., Письма в Журнал экспериментальной и теоретической физики 2024 Т. 120 № 8 С. 644–649
In this paper, we applied a deep neural network to study the issue of knowledge transferability between statistical mechanics models. The following computer experiment was conducted. A convolutional neural network was trained to solve the problem of binary classification of snapshots of snapshots of the location of spins of the Ising model on a two-dimensional ...
Added: September 25, 2024
[б.и.], 2023.
The Workshop will be held in the Meshcheryakov Laboratory of Information Technologies (MLIT) of the Joint Institute for Nuclear Research (JINR) on July 6-8, 2022.
The workshop primarily focuses on the use of machine learning in particle astrophysics and high energy physics, but is not limited to this area. Topics of interest are various applications of ...
Added: March 12, 2024
Лебедева А. Ю., В кн.: Одиннадцатая Международная конференция по компьютерной обработке тюркских языков «TurkLang 2023».: Каз.: Издательство Академии наук Республики Татарстан, 2023. С. 460–471.
Added: March 6, 2024
Sadrtdinov I., Dmitrii Pozdeev, Dmitry P Vetrov et al., , in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).: Curran Associates, Inc., 2023. P. 15936–15964.
Transfer learning and ensembling are two popular techniques for improving the performance and robustness of neural networks. Due to the high cost of pre-training, ensembles of models fine-tuned from a single pre-trained checkpoint are often used in practice. Such models end up in the same basin of the loss landscape, which we call the pre-train ...
Added: February 26, 2024
IITP RAS, 2023.
В сборнике представлены тезисы работ участников 11-ой Московской конференции по вычислительной молекулярной биологии MCCMB'23. Работы посвящены актуальным вопросам анализа аминокислотных и нуклеотидных последовательностей, структур биополимеров, молекулярной эволюции, методов высокопроизводительного секвенирования, системной биологии и биоалгоритмов. ...
Added: November 30, 2023
Cham: Springer, 2023.
Added: November 29, 2023
Malik M. S., Nazarova A., Mona M. J. et al., Journal of King Saud University - Computer and Information Sciences 2023 Vol. 35 No. 8 Article 101736
Hope Speech Detection (HSD) from social media is a new direction for promoting and supporting positive content to encourage harmony and positivity in society. As users of social media belong to different linguistic communities, hope speech detection is rarely studied as a multilingual task considering low-resource languages. Moreover, prior studies explored only monolingual techniques, and the ...
Added: November 22, 2023
Magaj G., Soroka A., Studies in Computational Intelligence, 2023.
The basis of transfer learning methods is the ability of deep neural networks to use knowledge from one domain to learn in another domain. However, another important task is the analysis and explanation of the internal representations of deep neural networks models in the process of transfer learning. Some deep models are known to be ...
Added: October 25, 2023
Alanov A., Titov V., Nakhodnov M. et al., , in: 2023 IEEE/CVF International Conference on Computer Vision (ICCV).: IEEE, 2023. P. 2184–2194.
Added: June 21, 2023