kras в таблицах

report.tex

@@ -200,6 +200,8 @@
         \hline
         A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Epithelial ovarian cancer (EOC) & Least absolute shrinkage and selection operator (LASSO) regression, logistic regression (LR) & Nearly 300 tissue samples, and other clinical data from Gene Expression Omnibus (GEO) database~\cite{geo}, transcriptomic sequencing data and clinical information from tumor tissues of 379 EOC patients from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & - \\
         \hline
+        Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~\cite{kras} & Lung cancer & Random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), decision tree (DT) & 7612 sample of gene expression profile data from Gene Expression Omnibus (GEO) database~\cite{geo}. Each sample contained the expression of 8687 genes & Seven feature ranking algorithms were applied, including least absolute shrinkage and selection operator (LASSO), light gradient boosting machine (LightGBM), monte carlo feature selection (MCFS), minimum redundancy maximum relevance (mRMR), random forest (RF) - based, categorical boosting (CATB), and extreme gradient boosting (XGB) \\
+        \hline
         \end{tabularx}
     \end{table}
     
@@ -234,6 +236,8 @@
         \hline
         A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} &  &  &  &  & + &  &  &  &  & + \\
         \hline
+        Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~\cite{kras} & + & + & + &  &  & + &  &  &  & \\
+        \hline
         \end{tabularx}
     \end{table}
 
@@ -269,7 +273,9 @@
         \hline
         \textbf{Article} & \textbf{Key results} & \textbf{Best algorithms} & \textbf{Metrics} \\
         \hline
-        A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Found that three immune-related proteins—CCR1, IGHV3-35, and CD72—along with the presence of postoperative residual tumors, are strong predictors of platinum resistance in EOC patients. Proposed a model that can predict the efficacy of initial platinum-based chemotherapy. & Least absolute shrinkage and selection operator (LASSO) regression and linear regression (LR) & Area under curve (AUC) of 0.864 \\
+        A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Found that three immune-related proteins—CCR1, IGHV3-35, and CD72—along with the presence of postoperative residual tumors, are strong predictors of platinum resistance in EOC patients. Proposed a model that can predict the efficacy of initial platinum-based chemotherapy & Least absolute shrinkage and selection operator (LASSO) regression and linear regression (LR) & Area under curve (AUC) of 0.864 \\
+        \hline
+        Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~\cite{kras} & Identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, associated with cancer progression and drug resistance. Have built efficient classifiers as the byproduct & Categorical boosting (CATB) for feature selection and support vector machine (SVM) for classification & Accuracy of 93.1\% and F1-score of 0.938 \\
         \hline
         \end{tabularx}
     \end{table}
@@ -306,6 +312,8 @@
         Jun Zhang, Youliang Weng, Yi Liu, Nan Wang, Shangyuan Feng, Sufang Qiu, Duo Lin, Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction, 2024.
         \bibitem{platinum}
         Shen S, Wang C, Gu J, Song F, Wu X, Qian F, Chen X, Wang L, Peng Q, Xing Z, Gu L, Wang F, Cheng X. A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles, 2024.
+        \bibitem{kras}
+        Xiandong Lin, QingLan Ma, Lei Chen, Wei Guo, Zhiyi Huang, Tao Huang, Yu-Dong Cai, Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods, 2023.
         \bibitem{cellprofile}
         T. Misteli, C. McQuin, A. Goodman, V. Chernyshev, L. Kamentsky, B.A. Cimini, et al., CellProfiler 3.0: next-generation image processing for biology, 2018.
         \bibitem{tcga}