Results table

report.tex

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         \end{tabularx}
     \end{table}
     
+    \newpage
+
+    \begin{table}[h!]
+        \centering
+        \caption{Results obtained in research papers.}
+        \footnotesize
+        \begin{tabularx}{\textwidth}{|X|X|X|X|}
+        \hline
+        \textbf{Article} & \textbf{Key results} & \textbf{Best algorithms} & \textbf{Metrics} \\
+        \hline
+        Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~\cite{paclitaxel} & Demonstrated that morphological changes in epithelial ovarian cancer (EOC) cells correlate with drug sensitivity, highlighting the potential for monitoring drug resistance.
+        & Support vector machine (SVM) and neural network (NN) & Accuracy of 94.5\% for SVM and 93.4\% for NN \\
+        \hline
+        Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~\cite{heterogeneity} & Demonstrated a strong correlation between intra-tumor heterogeneity (ITH) and drug resistance in epithelial ovarian cancer (EOC) cells & Least absolute shrinkage and selection operator (LASSO) regression & Area under curve (AUC) of 0.601, 0.594, and 0.589 for 1, 3, and 5 years survival time accordingly \\
+        \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} & Proposed a model  that incorporates mitochondria-related chemoradiotherapy resistance (MRCRTR) genes. Identified six mitochondria-related genes that affect CRT and the prognosis of esophageal cancer. & Neural network (NN) and least absolute shrinkage and selection operator (LASSO) regression & Root mean squared error (RMSE) of 0.001 for NN and 0.003 for LASSO \\
+        \hline
+        Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} & Developed a novel approach using label-free surface-enhanced Raman spectroscopy (SERS) to profile molecular patterns in the blood of nasopharyngeal cancer (NPC) patients, distinguishing those with radiotherapy sensitivity from those with resistance & Principal component analysis and linear discriminant analysis (PCA-LDA) & Accuracy of 96.7\% for identifying radiotherapy resistance subjects from sensitivity ones and 100\% for identifying the nasopharyngeal cancer (NPC) subjects from healthy ones \\
+        \hline
+        \end{tabularx}
+    \end{table}
+    
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