to generate and prepare
data for binary logistic
regression. To implement the described approaches, the Python
at an early age were revealed in 89 patients (78%). In 73 children (64%) anamnestic
data analysis revealed a
Kazantseva, Anastasiya,
Davydova, Yuliya,
Enikeeva, Renata,
Mustafin, Rustam,
Malykh, Sergey,
Lobaskova, Marina,
Kanapin, Alexander,
Prokopenko, Inga,
Khusnutdinova, Elza (2023) to address the “missing heritability” problem. We designed
regression models, which included PGS using 27
Kazantseva, A.,
Enikeeva, R.,
Takhirova, Z.,
Davydova, Y.,
Mustafin, R.,
Malykh, S.,
Karunas, A.,
Kanapin, A.,
Khusnutdinova, E. (2023) the genetic basis for SARS-CoV-2 infection susceptibility and severe COVID-19. However,
data on the genetic
outcome were studied by binary logistic
regression. Results. Univariate
analysis showed that the risk
Mukhtarova, Liliya A.,
Fedorova, Yulia Yu.,
Karunas, Aleksandra S.,
Prokofyeva, Darya S.,
Nurgalieva, Alfia Kh.,
Khusnutdinova, Elza K.,
Zagidullin, Shamil Z. (2023) ), and HRH3 (rs3787429) genes was performed by real-time polymerase chain reaction. Using
regression analysis, is provided, along with comparative
analysis of existing solutions in the field of DBMS, such as Oracle, My