univer-5th-semester/literature-review
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platinum в таблице с ml алгоритмами

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Артём Тищенко
2024-10-31 17:56:02 +03:00
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report.tex
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@@ -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!]
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