From 286dc21f2e9492f95d79086023e804b7733796d0 Mon Sep 17 00:00:00 2001
From: Arity-T <tish.mainmail@gmail.com>
Date: Thu, 5 Dec 2024 11:13:00 +0300
Subject: [PATCH] =?UTF-8?q?=D0=A2=D0=B0=D0=B1=D0=BB=D0=B8=D1=86=D1=8B=20?=
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 report.tex | 51 ++++++++++++++++++++++++++-------------------------
 1 file changed, 26 insertions(+), 25 deletions(-)

diff --git a/report.tex b/report.tex
index 8fc9ed3..c86a7e3 100644
--- a/report.tex
+++ b/report.tex
@@ -272,31 +272,6 @@
     
     Overall, the application of machine learning in assessing drug resistance represents a novel approach in cancer treatment, offering lots of opportunities to enhance the precision and effectiveness of therapies. By continuing to advance machine learning algorithms and support their integration into clinical practice, the medical community can significantly improve the management of drug-resistant cancers, ultimately reducing mortality rates and improving the quality of life for patients worldwide.
 
-    \addtocounter{table}{1}
-    \includepdf[pages={1}, fitpaper, pagecommand={
-        \thispagestyle{empty}
-            \begin{tikzpicture}[remember picture, overlay]
-                \node at (current page.north) [anchor=north, yshift=-25pt] {
-                    \begin{minipage}{3.38\textwidth}
-                        Table 1. Methods used in research papers. Abbreviations: Epithelial Ovarian Cancer (EOC), ESophageal Cancer (ESC), NaSopharyngeal Cancer (NSC), Lung Cancer (LC), Pancreatic Cancer (PC), Cervical Cancer (CC), Tongue Cancer (TG), Breast Cancer (BC), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Logistic Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).
-                    \end{minipage}
-                };
-            \end{tikzpicture}
-    }]{methods_table/methods.pdf}    
-
-
-    \addtocounter{table}{1}
-    \includepdf[pages={1}, fitpaper, pagecommand={
-    \thispagestyle{empty}
-        \begin{tikzpicture}[remember picture, overlay]
-            \node at (current page.north) [anchor=north, yshift=-20pt] {
-                \begin{minipage}{1.63\textwidth}
-                    Table 2. Results obtained in research papers. Abbreviations: Area under curve (AUC), Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC), Matthew's correlation coefficient (MCC).
-                \end{minipage}
-            };
-        \end{tikzpicture}
-    }]{results_table/results.pdf}
-
 
     \newpage
     \vspace{-1.5cm}
@@ -352,4 +327,30 @@
         \bibitem{PerkinElmer}
         “PerkinElmer | Science with purpose.” \url{https://content.perkinelmer.com/} (accessed Dec. 01, 2024).
     \end{thebibliography}
+
+
+    \addtocounter{table}{1}
+    \includepdf[pages={1}, fitpaper, pagecommand={
+        \thispagestyle{empty}
+            \begin{tikzpicture}[remember picture, overlay]
+                \node at (current page.north) [anchor=north, yshift=-25pt] {
+                    \begin{minipage}{3.38\textwidth}
+                        Table 1. Methods used in research papers. Abbreviations: Epithelial Ovarian Cancer (EOC), ESophageal Cancer (ESC), NaSopharyngeal Cancer (NSC), Lung Cancer (LC), Pancreatic Cancer (PC), Cervical Cancer (CC), Tongue Cancer (TG), Breast Cancer (BC), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Logistic Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).
+                    \end{minipage}
+                };
+            \end{tikzpicture}
+    }]{methods_table/methods.pdf}    
+
+
+    \addtocounter{table}{1}
+    \includepdf[pages={1}, fitpaper, pagecommand={
+    \thispagestyle{empty}
+        \begin{tikzpicture}[remember picture, overlay]
+            \node at (current page.north) [anchor=north, yshift=-20pt] {
+                \begin{minipage}{1.63\textwidth}
+                    Table 2. Results obtained in research papers. Abbreviations: Area under curve (AUC), Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC), Matthew's correlation coefficient (MCC).
+                \end{minipage}
+            };
+        \end{tikzpicture}
+    }]{results_table/results.pdf}
 \end{document}
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