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\begin{document} % начало документа
    
    % НАЧАЛО ТИТУЛЬНОГО ЛИСТА
    \begin{center}
        \hfill \break
        \hfill \break
        \normalsize{MINISTRY OF SCIENCE AND HIGHER EDUCATION OF THE RUSSIAN FEDERATION\\
        Federal State Autonomous Educational Institution of Higher Education Peter the Great St. Petersburg Polytechnic University\\[10pt]}
        \normalsize{Institute of Computer Science and Cybersecurity}\\[10pt] 
        \normalsize{Higher School of Artificial Intelligence Technology}\\[10pt] 
        \normalsize{Direction 02.03.01 Mathematics and computer Science}\\
        
        \hfill \break
        \hfill \break
        \hfill \break
        \hfill \break
        \large{\textbf{Literature Review}}\\
        \large{\textit{Machine learning approaches for assessing drug resistance in cancer treatment}}\\
        
        \hfill \break
        \hfill \break
    \end{center}
    
    \small{ 
        \begin{tabular}{lrrl}
            \!\!\!Student, & \hspace{2cm} & & \\
            \!\!\!group 5130201/20102 & \hspace{2cm} & \underline{\hspace{3cm}} &Tishenko А. А. \\\\
            \!\!\!Supervisor, Ph. D. & \hspace{2cm} &  \underline{\hspace{3cm}} &  Motorin D. E. \\\\
            &&\hspace{4cm}
        \end{tabular}
        \begin{flushright}
            <<\underline{\hspace{1cm}}>>\underline{\hspace{2.5cm}} 2024г.
        \end{flushright}
    }
    
    \hfill \break
    % \hfill \break
    \begin{center} \small{Saint-Petersburg, 2024} \end{center}
    \thispagestyle{empty} % выключаем отображение номера для этой страницы
    
    % КОНЕЦ ТИТУЛЬНОГО ЛИСТА
    \newpage
    
    % \tableofcontents
    % \newpage
    
    \section*{Introduction}
    \addcontentsline{toc}{section}{Introduction}
    Progress has been made in chemotherapy drugs, but drug resistance remains a major challenge in cancer treatment and the main cause of cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Existing drug sensitivity assessment methods has limitations such as low modeling success rates, high cost, and time-consuming process. Machine learning is both an expanding and evolving field of computing, and it seems that it can significantly help in solving chemotherapy resistance problem. Here we provide an overview of how different studies apply machine learning algorithms to predict and understand chemotherapy resistance in various cancer types. Also we consider the strengths and limitations of each approach and discuss obtained results.

    % \newpage
    
    % \section {Первый раздел}
    % \subsection{Первый подраздел}
    % Текст первого подраздела

    % \section*{Conclusion}
    % \addcontentsline{toc}{section}{Conclusion}
    % Conclusion text

    \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}