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report.tex

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     In \cite{kras}, authors firstly applied machine learning algorithms to extract most important features and created seven feature lists, after that they applied four classification algorithms. Their best result was achieved with CATBoost feature list and support vector machine as classification algorithms (accuracy of 93.1\%). Also after analysing recieved feature lists authors were able to identify top genes associated with tumor progression and drug resistance (H2AFZ, CKS1B, TUBA1B, RRM2, BIRC5).
 
+    The study \cite{cervical} employed a Random Forest model utilizing genomic features. The model successfully predicted the response to platinum-based neoadjuvant chemotherapy in patients with locally advanced cervical cancer (LACC). However, the main focus of the study was not on building the model but on analyzing feature importance to identify key genes associated with chemoresistance. Through importance analysis, the authors identified that the top three significant single nucleotide polymorphisms (SNPs)—rs4558508, rs1130233, and rs7259541—were all located within the Akt gene family. Specifically, patients carrying the heterozygous GA genotype in Akt2 rs4558508 had a significantly increased risk of chemoresistance compared to those with GG or AA genotypes.
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+    The authors of \cite{tabular} developed a deep learning model using the TabNet algorithm to predict cisplatin sensitivity based on cisplatin-perturbed gene expression data. Their model achieved over 80\% accuracy, surpassing a variety of other machine learning algorithms such as ridge regression, lasso, elastic net, Nu-SVC, XGBoost, and random forest. The TabNet model consistently demonstrated strong predictive performance with an average AUC of 0.808 across 500 different sample splits. By analyzing feature importance, the authors identified several key genes contributing to cisplatin resistance, most notably BCL2L1. The upregulation of BCL2L1, along with genes like CCND1 and PLK2, was associated with poor survival in ovarian cancer patients, highlighting potential targets for overcoming drug resistance. These findings are in line with the results of \cite{kras}, where important genes associated with tumor progression and drug resistance were also identified using machine learning feature selection techniques.
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+    In articles \cite{heterogeneity}, \cite{mitochondria}, \cite{glut}, the authors used machine learning for a different tasks. In~\cite{heterogeneity} used machine learning algorithms from the specialized software CellProfiler~\cite{cellprofile} to extract quantitative image features and then performed statistical analysis of feature importance. The authors of~\cite{mitochondria} and~\cite{glut} applied machine learning algorithms for the regression task and proposed their own scores, mitochondria related chemoradiotherapy resistance (MRCRTR) score and machine learning-derived immunosenescence-related score (MLIRS), respectively. 
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+    The study \cite{heterogeneity} demonstrated that specific computational pathomic signatures extracted from histopathological images can effectively predict drug resistance in ovarian cancer patients. By analyzing 1212 statistical image features derived from whole-slide images, the authors identified 26 key features related to patient survival. Among these, the Perimeter.sd feature, which measures the standard deviation of nuclear perimeter, stood out as the most significant predictor. A higher Perimeter.sd value was positively correlated with increased intra-tumor heterogeneity and was associated with a higher risk of platinum-based chemotherapy resistance.
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+    The authors of \cite{mitochondria} developed a prognostic model based on mitochondria-related chemoradiotherapy resistance (MRCRTR) genes to predict survival outcomes in esophageal cancer patients. They identified six key genes (CTSL, TBL1X, CLN8, MMP1, PDPN, and MRPL37) that have high diagnostic value for chemoradiotherapy resistance. The MRCRTR score derived from these genes showed that patients with high scores had significantly lower survival rates than those with low scores (log-rank test, $p < 0.001$). Cox regression analyses confirmed the MRCRTR score as an independent prognostic factor. Additionally, the MRCRTR score was significantly correlated with increased expression of immune checkpoints and higher angiogenesis, epithelial-mesenchymal transition (EMT), and cancer-associated fibroblast (CAF) scores. 
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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.
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