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\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}

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