kras в feature analysis

report.tex

@@ -197,6 +197,8 @@
 
     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.
     
+    In \cite{kras} feature importance analysis was employed to identify genes associated with resistance to KRAS G12C inhibitor treatment in cancer cells. The authors used seven different feature ranking algorithms: LASSO, LightGBM, MCFS, mRMR, RF-based, CATBoost, and XGBoost. These algorithms generated feature lists based on different principles, enabling a comprehensive evaluation of gene significance. To refine the feature selection, the authors applied Incremental Feature Selection (IFS), testing the performance of classifiers like Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM) on the ranked features. By doing feature analysis they were able to highlight several key genes, such as H2AFZ, CKS1B, and TUBA1B, which were consistently ranked highly across multiple algorithms and are linked to tumor progression and drug resistance.
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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.