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     In article~\cite{platinum}, authors prepared their own dataset and also used open databases. In this study, 4D data-independent acquisition (DIA) proteomic sequencing was performed on tissue-derived extracellular vesicles (tsEVs) obtained from 58 platinum-sensitive and 30 platinum-resistant patients with EOC. Also authors used the GSE15372, GSE33482, GSE26712 and GSE63885 microarray datasets from the Gene Expression Omnibus database~\cite{geo}. GSE15372 and GSE33482 represent EOC cell line-derived RNA microarray datasets, comprising 5 and 5 and 6 and 6 platinum-sensitive and resistant cell line samples, respectively. GSE26712 and GSE63885 involve clinical and sequencing data for 195 and 101 EOC patients, respectively. Additionally, transcriptomic sequencing data and clinical information from the tumour tissues of 379 patients with EOC, sourced from the TCGA database~\cite{tcga}, was used.
 
-    % \section{Feature analysis}    
+    \section{Feature importance analysis} 
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+    Machine learning algorithms are often regarded as black boxes, providing powerful predictions but limited insight into the underlying mechanisms driving those predictions. In fields such as medicine and biology, however, interpretability is not just a desirable feature—it is a necessity. Transparent models and interpretable outputs are critical for ensuring that predictions and recommendations can be explained, validated, and trusted, especially when they influence life-altering decisions. In the context of cancer treatment, this need for interpretability becomes even more pressing. Misguided treatment decisions can delay the administration of effective therapies, significantly increasing the risk of mortality. Beyond guiding clinical decisions, the interpretability of features in machine learning models also offers a unique opportunity to deepen our understanding of drug resistance in cancer. By unraveling the biological and molecular underpinnings of resistance, we can develop more targeted and effective therapeutic strategies.
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     \section{Results}
     In all works, the construction of machine learning models is essentially a secondary result. First of all, studies show the applicability of these methods to tasks related to the problems of cancer cell resistance to chemotherapy. Also, using machine learning methods, the authors test their hypotheses, confirm or discover links between various characteristics of cancer cells, patient clinical data and drug resistance.