univer-5th-semester/literature-review
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Добавил glut в таблицу с алгоритмами

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Артём Тищенко
2024-11-03 20:15:53 +03:00
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ml_table/ml_table.tex
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\centering \centering
\caption*{Table 2. Machine learning algorithms comparison.} \caption*{Table 2. Machine learning algorithms comparison.}
\footnotesize \footnotesize
\begin{tabularx}{\textwidth}{|p{6cm}|X|X|X|X|X|X|X|X|X|X|} \begin{tabularx}{\textwidth}{|p{9cm}|X|X|X|X|X|X|X|X|X|X|X|X|X|X|}
\hline \hline
\textbf{Article} & \textbf{DT} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} & \textbf{LR} \\ \textbf{Article} & \textbf{DT} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} & \textbf{LR} & \textbf{Cox}
& \textbf{SuperPC} & \textbf{Enet} & \textbf{GBM}\\
\hline \hline
Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~[1] & + & & + & + & + & & & & & \\ Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~[1] & + & + & + & + & & & & & & & & & & \\
\hline \hline
Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~[2] & & & & & + & & & & & \\ Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~[2] & & & & & + & & & & & & & & & \\
\hline \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] & & + & + & + & + & + & & + & + & \\ 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 \hline
Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~[4] & & & & & & & + & & & \\ Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~[4] & & & & & & & + & & & & & & & \\
\hline \hline
A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~[5] & & & & & + & & & & & + \\ A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~[5] & & & & & + & & & & & + & & & & \\
\hline \hline
Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~[6] & + & + & + & & & + & & & & \\ Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~[6] & + & + & + & & & + & & & & & & & & \\
\hline
Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance~[7] & & & + & & + & & & & & & + & + & + & + \\
\hline \hline
\end{tabularx} \end{tabularx}
\end{table} \end{table}