Development of a method for recognizing biomedical entities in the texts of scientific articles

Poster (download) Stepan Derevyanchenko1, Pavel Demenkov21Novosibirsk State University, sod97@yandex.ru2The Federal Research Center Institute of Cytology and Genetics The Siberian Branch of the Russian Academy of Sciences, demps@bionet.nsc.ru In this paper, we consider the problem of name entity recognition in the texts of biological scientific articles. Using a combination of machine learning methods allowed us to achieve high recognition quality indicators.

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Development and analysis of AIDS epidemic agent-based computer model applying an algorithm for explicit calculation of HIV replicability

Poster (download) Anna Smirnova1, Mikhail Ponomarenko2, Sergey Lashin31ICG SB RAS, Novosibirsk, Russia NSU, Novosibirsk, Russia, asmirnova@bionet.nsc.ru2ICG SB RAS, Novosibirsk, Russia, pon@bionet.nsc.ru3ICG SB RAS, Novosibirsk, Russia NSU, Novosibirsk, Russia, lashin@bionet.nsc.ru Different strains of HIV contribute differently to the course of the disease. For its evaluation, 1,336 HIV strains were analyzed. An agent model of the spread of HIV infection in the population has been developed. We analyzed 5 scenarios for the development of the HIV epidemic in Russia, depending on the initial data. Without additional measures, after 10 years, the percentage of HIV patients in Russia will increase from 1% to 2.45%. Comprehensive measures to increase the use of barrier contraception, reduce the number of joint injections among drug users and improve the situation with treatment coverage can reduce the percentage of patients from 1% to 0.3% and prevent the emergence of new patients.

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