references

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2024-10-15 13:58:19 +03:00
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\begin{tabular}{lrrl}
\!\!\!Student, & \hspace{2cm} & & \\
\!\!\!group 5130201/20102 & \hspace{2cm} & \underline{\hspace{3cm}} &Tishenko А. А. \\\\
\!\!\!Supervisor & \hspace{2cm} & \underline{\hspace{3cm}} & Motorin D. E. \\\\
\!\!\!Supervisor, Ph. D. & \hspace{2cm} & \underline{\hspace{3cm}} & Motorin D. E. \\\\
&&\hspace{4cm}
\end{tabular}
\begin{flushright}
@@ -134,9 +134,23 @@
% \subsection{Первый подраздел}
% Текст первого подраздела
% \section*{Заключение}
% \addcontentsline{toc}{section}{Заключение}
% \section*{Conclusion}
% \addcontentsline{toc}{section}{Conclusion}
% Conclusion text
% Текст заключения
\newpage
% \section*{Literature}
% \addcontentsline{toc}{section}{Literature}
\vspace{-1.5cm}
\begin{thebibliography}{0}
\bibitem{paclitaxel}
Lu Xin, Wen Xiao, Huanzhi Zhang, Yakun Liu, Xiaoping Li, Pietro Ferraro, Feng Pan, Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning, 2024.
\bibitem{heterogeneity}
Qiuli Zhu, Hua Dai, Feng Qiu, Weiming Lou, Xin Wang, Libin Deng, Chao Shi, Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer, 2024.
\bibitem{mitochondria}
Ziyu Liu, Zahra Zeinalzadeh, Tao Huang, Yingying Han, Lushan Peng, Dan Wang, Zongjiang Zhou, DIABATE Ousmane, Junpu Wang, Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer, 2024.
\bibitem{sers}
Jun Zhang, Youliang Weng, Yi Liu, Nan Wang, Shangyuan Feng, Sufang Qiu, Duo Lin, Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction, 2024.
\end{thebibliography}
\end{document}