From 3202c9e4e86970371e0834cccab58244198de530 Mon Sep 17 00:00:00 2001
From: Arity-T <tish.mainmail@gmail.com>
Date: Thu, 31 Oct 2024 17:56:02 +0300
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 report.tex | 35 +++++++++++++++++++++++++++++------
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diff --git a/report.tex b/report.tex
index 1796c7b..5f80129 100644
--- a/report.tex
+++ b/report.tex
@@ -208,21 +208,36 @@
         \centering
         \caption{Machine learning algorithms comparision.}
         \footnotesize
-        \begin{tabularx}{\textwidth}{|p{3cm}|c|c|c|c|c|c|c|X|c|c|}
+        \begin{tabularx}{\textwidth}{|p{3cm}|c|c|c|c|c|c|X|c|c|c|}
         \hline
-        \textbf{Article} & \textbf{DT} & \textbf{NB} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} \\
+        \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} & + & + & + & + & + &  &  &  &  & \\
+        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} &  &  &  &  &  & + &  &  &  & \\
+        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} &  &  & + & + & + & + & + &  & + & + \\
+        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} &  &  &  &  &  &  &  & + &  & \\
+        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
+        \end{tabularx}
+    \end{table}
+
+
     \newpage
 
     \begin{table}[h!]
@@ -263,6 +278,14 @@
     % \addcontentsline{toc}{section}{Conclusion}
     % Conclusion text
 
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