Ещё две статьи

report.tex

@@ -193,7 +193,9 @@
 
     In \cite{tabular} authors also used SHAP to performe feature analysis. In this study, feature importance analysis was utilized to identify key genes associated with cisplatin resistance. By leveraging feature importance derived from multiple predictive models, the authors highlighted BCL2L1 as a critical gene mediating cisplatin resistance. Notably, earlier studies suggested that $\beta$-catenin expression is necessary for maintaining elevated levels of BCL2L1 in cisplatin-resistant cells. Additionally, the analysis revealed the involvement of PLK2, whose expression was linked to $\beta$-catenin activity. The authors demonstrated that lower expression levels of BCL2L1 and PLK2 correlated with improved outcomes in ovarian cancer, and inhibitors targeting these genes showed a synergistic effect when combined with cisplatin. These findings underscore the importance of the $\beta$-catenin/BCL2L1 axis in driving resistance and provide potential targets for enhancing cisplatin efficacy.
 
+    In study \cite{heterogeneity}, the authors employed feature importance analysis to identify key histopathological features associated with intratumoral heterogeneity (ITH), drug resistance, and prognosis in ovarian cancer. The analysis was conducted using the R programming environment~\cite{r-lang}, leveraging tools such as the "limma" package for differential analysis and the "glmnet" package for LASSO regression modeling. 924 features were identified as differentially expressed between cancerous and non-cancerous tissues. Of these, 394 features were associated with overall survival, and 26 key features were identified at the intersection of survival analysis and differential expression.
 
+    The authors of \cite{mitochondria} also used R programming environment to performe feature importance analysis to identify key predictor genes for mitochondrial-related CRT resistance (MRCRTR). They used the DALEX~\cite{dalex}, an R package for model interpretability, to analyze feature importance and residual distribution, which helps interpret how different features influence model predictions. This tool provided insights into the contribution of each predictor gene across the machine learning models. The top 12 genes identified through this analysis were selected as MRCRTR predictor genes, contributing to the development of a prognostic model for esophageal cancer.
     
     \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.