Убрал старую таблицу methods

report.tex

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     }]{methods_table/methods.pdf}    
 
 
-    \newpage
-    \begin{table}[h!]
-        \centering
-        \caption{Methods used in research papers.}
-        \footnotesize
-        \begin{tabularx}{\textwidth}{|X|p{2cm}|X|X|X|}
-        \hline
-        \textbf{Article} & \textbf{Cancer type} & \textbf{Machine learning algorithms} & \textbf{Datasets} & \textbf{Feature importance analysis} \\
-        \hline
-        Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~\cite{paclitaxel} & Epithelial ovarian cancer (EOC) & Tree, Naive Bayes, K-nearest neighbors
-        (KNN), support vector machine (SVM), and neural network (NN) & Self-produced dataset of 2998 quantitative phase images (QPIs) of EOC cells & SHapley Additive
-        exPlanations (SHAP), Pearson coefficient, Kruskal-Wallis test \\
-        \hline
-        Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~\cite{heterogeneity} & Epithelial ovarian cancer (EOC) & CellProfiler~\cite{cellprofile}, least absolute shrinkage and selection operator (LASSO) regression & 494  ovarian and 70 paracarcinoma tissues images from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & Statistical analysis using R~\cite{r-lang}. Various visualizations, including heatmaps, Venn diagrams, ROC curves, and survival curves. \\    
-        \hline
-        Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer~\cite{mitochondria} & Esophageal cancer & Generalized linear model (GLM), K-nearest neighbor (KNN),  least absolute shrinkage and selection operator (LASSO) regression, neural network (NN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB) & Nearly 500 tissue samples, RNA-sequences and some other clinical data from Gene Expression Omnibus (GEO) database~\cite{geo}, information on 183 esophageal cancer patients from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & Statistical analysis using DALEX package~\cite{dalex} for~R~\cite{r-lang} \\
-        \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
-    \end{tabularx}
-\end{table}
-
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-\begin{table}[h!]
-    \centering
-    \caption{Methods used in research papers (continued).}
-    \footnotesize
-    \begin{tabularx}{\textwidth}{|X|p{2cm}|X|X|X|}
-        \hline
-        \textbf{Article} & \textbf{Cancer type} & \textbf{Machine learning algorithms} & \textbf{Datasets} & \textbf{Feature importance analysis} \\
-        \hline
-        Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} & Nasopharyng-eal cancer &  Principal component analysis and linear discriminant analysis (PCA-LDA) & Self-produced dataset of 120 plasma samples, 60 of which from healthy volunteers, 30 from radiotherapy sensitivity patients and 30 from radiotherapy resistance patients & \\
-        \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
-        Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance~\cite{glut} & Pancreatic cancer & Support vector machine (SVM), CoxBoost, random forest (RF), least absolute shrinkage and selection operator (LASSO), stepwise Cox, partial least squares regression for Cox (plsRcox), Ridge, supervised principal components (SuperPC), elastic network (Enet), generalized boosted regression modeling (GBM) & Nearly 1000 samples from 12 datasets from The Cancer Genome Atlas (TCGA)~\cite{tcga}, Gene Expression Omnibus (GEO)~\cite{geo} and The European Genome-phenome Archive (EGA)~\cite{ega} & The univariate Cox regression analysis was used to identify immunosenescence-related genes with prognostic significance in pancreatic cancer. Genes with a p-value of less than 0.01 were selected as meaningful features for subsequent analysis \\
-        \hline
-        \end{tabularx}
-    \end{table}
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