Conclusion дописал

report.tex

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     The authors of \cite{glut} identified two immunosenescence-associated phenotypes (IMSP1 and IMSP2) with significant differences in prognosis and immune cell infiltration. The authors constructed a Machine-Learning Immunosenescence-Related Scoring (MLIRS) system using a combination of stepwise Cox regression and generalized boosted regression modeling (GBM), integrating multiple machine learning algorithms across 101 cross-validation methods. Their MLIRS model demonstrated robust prognostic performance with an Area Under Curve (AUC) of 0.91. They found that patients with high MLIRS scores had worse prognosis and lower abundance of immune cell infiltration, whereas those with low MLIRS scores showed better sensitivity to chemotherapy and immunotherapy.
 
+    \newpage
+    \section*{Conclusion}
+    \addcontentsline{toc}{section}{Conclusion}
+    In conclusion, machine learning approaches have emerged as pivotal tools in the assessment of drug resistance in cancer treatment, addressing one of the most formidable challenges in oncology. The integration of machine learning algorithms into cancer research and clinical practice has facilitated the identification of critical genetic, molecular, and morphological features associated with drug resistance. Reviewed studies have shown that machine learning models can accurately classify and predict resistance patterns, enabling earlier intervention and more personalized therapeutic strategies. The authors applied machine learning to analyze diverse types of data, from gene expression profiles to histopathological images. Techniques such as SHapley Additive exPlanations (SHAP)~\cite{shap} and least absolute shrinkage and selection operator (LASSO)~\cite{lasso} have improved the interpretability of the models, transforming them from "black boxes" into transparent systems that provide meaningful insights into the mechanisms underlying drug resistance. This transparency is crucial for clinical applications, as it allows to understand and trust the model's predictions, ultimately guiding more informed decision-making processes.
+    
+    Despite the significant progress, several challenges remain. The complexity of cancer biology necessitates the continual refinement of the algorithms to improve their accuracy and generalizability across different cancer types and patient populations, e. g. most of the reviewed studies focus on Chinese populations. Additionally, the integration of machine learning tools into clinical workflows requires robust validation and the establishment of standardized protocols to ensure consistency and reliability in diverse healthcare settings. Future research should focus on collecting more data, making models easier to understand, and encouraging collaboration between different fields to effectively apply computational advances in clinical scenarios.
+    
+    Overall, the application of machine learning in assessing drug resistance represents a novel approach in cancer treatment, offering lots of opportunities to enhance the precision and effectiveness of therapies. By continuing to advance machine learning algorithms and support their integration into clinical practice, the medical community can significantly improve the management of drug-resistant cancers, ultimately reducing mortality rates and improving the quality of life for patients worldwide.
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     }]{results_table/results.pdf}
 
 
-    % \section*{Conclusion}
-    % \addcontentsline{toc}{section}{Conclusion}
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-    % \section*{Literature}
-    % \addcontentsline{toc}{section}{Literature}
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         \bibitem{paclitaxel}