статья platinum таблица по методам

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2024-10-31 12:12:24 +03:00
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\begin{table}[h!]
\centering
\caption{Methods used in research papers (continued).}
\footnotesize
\begin{tabularx}{\textwidth}{|X|p{2cm}|X|X|X|}
\hline
\textbf{Article} & \textbf{Cancer type} & \textbf{Machine learning algorithms} & \textbf{Datasets} & \textbf{Feature importance analysis} \\
\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} & Epithelial ovarian cancer (EOC) & Least absolute shrinkage and selection operator (LASSO) regression, logistic regression (LR) & Nearly 300 tissue samples, and other clinical data from Gene Expression Omnibus (GEO) database~\cite{geo}, transcriptomic sequencing data and clinical information from tumor tissues of 379 EOC patients from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & - \\
\hline
\end{tabularx}
\end{table}
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\begin{table}[h!]
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% & \textbf{PCA-LDA}
\newpage
\begin{table}[h!]
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Ziyu Liu, Zahra Zeinalzadeh, Tao Huang, Yingying Han, Lushan Peng, Dan Wang, Zongjiang Zhou, DIABATE Ousmane, Junpu Wang, 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, 2024.
\bibitem{sers}
Jun Zhang, Youliang Weng, Yi Liu, Nan Wang, Shangyuan Feng, Sufang Qiu, Duo Lin, Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction, 2024.
\bibitem{platinum}
Shen S, Wang C, Gu J, Song F, Wu X, Qian F, Chen X, Wang L, Peng Q, Xing Z, Gu L, Wang F, Cheng X. A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles, 2024.
\bibitem{cellprofile}
T. Misteli, C. McQuin, A. Goodman, V. Chernyshev, L. Kamentsky, B.A. Cimini, et al., CellProfiler 3.0: next-generation image processing for biology, 2018.
\bibitem{tcga}