Таблица с алгоритмами на А3

.gitignore

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 !*.tex
 !img
 !img/**
+!ml_table
+!ml_table/*.tex

ml_table/ml_table.tex

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+\documentclass{article}
+\usepackage[14pt]{extsizes}
+\usepackage[T2A]{fontenc}
+\usepackage[utf8]{inputenc}
+\usepackage[a3paper, landscape, left=25mm, top=20mm, right=20mm, bottom=20mm, footskip=10mm]{geometry}
+\usepackage{tabularx}
+\usepackage{caption}
+\usepackage{graphicx}
+\usepackage{array}
+
+\renewcommand{\arraystretch}{1.4} % изменяю высоту строки в таблице
+
+\begin{document}
+
+\setcounter{page}{8}
+
+\begin{table}[h!]
+    \centering
+    \caption*{Table 2. Machine learning algorithms comparison.}
+    \footnotesize
+    \begin{tabularx}{\textwidth}{|p{6cm}|X|X|X|X|X|X|X|X|X|X|}
+    \hline
+    \textbf{Article} & \textbf{DT} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} & \textbf{LR} \\
+    \hline
+    Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~[1] & + & & + & + & + &  &  &  &  & \\
+    \hline
+    Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~[2] &  &  &  &  & + &  &  &  &  & \\
+    \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~[3] &  & + & + & + & + & + &  & + & + & \\
+    \hline
+    Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~[4] &  &  &  &  &  &  & + &  &  & \\
+    \hline
+    A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~[5] &  &  &  &  & + &  &  &  &  & + \\
+    \hline
+    Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~[6] & + & + & + &  &  & + &  &  &  & \\
+    \hline
+    \end{tabularx}
+\end{table}
+
+\end{document}

report.tex

@@ -14,6 +14,8 @@
 \usepackage{moreverb} %для печати в листинге исходного кода программ
 \usepackage{graphicx}
 
+\usepackage{pdfpages}
+
 \usepackage{array}
 \usepackage{multirow}
 
@@ -167,7 +169,7 @@
     % \section{Results}
 
 
-
+    \newpage
     \begin{table}[h!]
         \centering
         \caption{Methods used in research papers.}
@@ -189,6 +191,7 @@
     \end{tabularx}
 \end{table}
 
+\newpage
 \addtocounter{table}{-1}
 \begin{table}[h!]
     \centering
@@ -208,40 +211,10 @@
     \end{table}
 
     \newpage
-    \begin{table}[h!]
-        \centering
-        \caption{Machine learning algorithms comparision.}
-        \footnotesize
-        \begin{tabularx}{\textwidth}{|p{3cm}|c|c|c|c|c|c|X|c|c|c|}
-        \hline
-        \textbf{Article} & \textbf{DT} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} & \textbf{LR} \\
-        \hline
-        Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~\cite{paclitaxel} & + & & + & + & + &  &  &  &  & \\
-        \hline
-        Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~\cite{heterogeneity} &  &  &  &  & + &  &  &  &  & \\
-        \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} &  & + & + & + & + & + &  & + & + & \\
-        \hline
-        Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} &  &  &  &  &  &  & + &  &  & \\
-        \hline
-        \end{tabularx}
-    \end{table}
-
-    \addtocounter{table}{-1}
-    \begin{table}[h!]
-        \centering
-        \caption{Machine learning algorithms comparision (continued).}
-        \footnotesize
-        \begin{tabularx}{\textwidth}{|p{3cm}|c|c|c|c|c|c|X|c|c|c|}
-        \hline
-        \textbf{Article} & \textbf{DT} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} & \textbf{LR} \\
-        \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}
+    \addtocounter{table}{1}
+    \includepdf[pages={1}, fitpaper, pagecommand={
+    \thispagestyle{empty}
+    }]{ml_table/ml_table.pdf}
 
 
     \newpage
@@ -266,6 +239,7 @@
         \end{tabularx}
     \end{table}
 
+    \newpage
     \addtocounter{table}{-1}
     \begin{table}[h!]
         \centering
@@ -288,18 +262,6 @@
     % \addcontentsline{toc}{section}{Conclusion}
     % Conclusion text
 
-    \newpage
-    \phantom{text}
-    \newpage
-    \phantom{text}
-    \newpage
-    \phantom{text}
-    \newpage
-    \phantom{text}
-    \newpage
-    \phantom{text}
-    \newpage
-    \phantom{text}
     \newpage
     % \section*{Literature}
     % \addcontentsline{toc}{section}{Literature}