Полигенная оценка риска с помощью моделей глубинного обучения

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Полигенная оценка риска с помощью моделей глубинного обучения

Perelygin V., Poptsova M., Камелин А. В., Ракитько А. С.

Language: Russian

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Keywords: Машинное обучение в Биоинформатике Биоинформатика и медицина

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Proceedings of 11th Moscow Conference on Computational Molecular Biology MCCMB'23

IITP RAS, 2023.

GENA-LM: a family of open-source foundational DNA language models for long sequences

Fishman V., Kuratov Y., Shmelev A. et al., Nucleic Acids Research 2025 Vol. 53 No. 2 Article gkae1310

Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To ...

Added: August 1, 2025

Параллельные вычислительные технологии – XVII всероссийская конференция с международным участием, ПаВТ'2023, г. Санкт-Петербург, 28–30 марта 2023 г. Короткие статьи и описания плакатов

Издательский центр ЮУрГУ, 2023.

Данный сборник содержит статьи, включенные в программу всероссийской научной конференции с международным участием «Параллельные вычислительные технологии 2023». Конференция проходила 28–30 марта 2023 года в Университете ИТМО (г. СанктПетербург). Подробную информацию о конференции можно найти в сети Интернет по адресу http://agora.guru.ru/pavt. Отпечатано с авторских оригиналов. ...

Added: November 27, 2023

Randomness in Cancer Breakpoint Prediction

Cheloshkina K., Bzhikhatlov I., Poptsova M., Journal of Computational Biology 2021 Vol. 28 No. 7 P. 716–731

Cancer genomes are susceptible to multiple rearrangements by deleting, inserting, and translocating genomic regions. Recently, the problem of finding determinants of breakpoint formations was approached with machine learning methods; however, unlike cancer point mutations, breakpoint prediction appeared to be a more difficult task, and various machine learning models did not achieve high prediction power often ...

Added: September 10, 2021

ColocML: machine learning quantifies co-localization between mass spectrometry images

Ovchinnikova K., Lachlan S., Rakhlin A. et al., Bioinformatics 2020 P. 1–10

Motivation Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. Results We ...

Added: March 15, 2020