44 lines
2.4 KiB
TeX
44 lines
2.4 KiB
TeX
\documentclass{article}
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\usepackage[14pt]{extsizes}
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\usepackage[T2A]{fontenc}
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\usepackage[utf8]{inputenc}
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\usepackage[a3paper, landscape, left=25mm, top=20mm, right=20mm, bottom=20mm, footskip=10mm]{geometry}
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\usepackage{tabularx}
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\usepackage{caption}
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\usepackage{graphicx}
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\usepackage{array}
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\renewcommand{\arraystretch}{1.4} % изменяю высоту строки в таблице
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\begin{document}
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\setcounter{page}{8}
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\begin{table}[h!]
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\centering
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\caption*{Table 2. Machine learning algorithms comparison.}
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\footnotesize
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\begin{tabularx}{\textwidth}{|p{9cm}|X|X|X|X|X|X|X|X|X|X|X|X|X|X|}
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\hline
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\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}
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& \textbf{SuperPC} & \textbf{Enet} & \textbf{GBM}\\
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\hline
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Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~[1] & + & + & + & + & & & & & & & & & & \\
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\hline
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Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~[2] & & & & & + & & & & & & & & & \\
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\hline
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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] & & + & + & + & + & + & & + & + & & & & & \\
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\hline
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Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~[4] & & & & & & & + & & & & & & & \\
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\hline
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A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~[5] & & & & & + & & & & & + & & & & \\
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\hline
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Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~[6] & + & + & + & & & + & & & & & & & & \\
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\hline
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Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance~[7] & & & + & & + & & & & & & + & + & + & + \\
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\hline
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\end{tabularx}
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\end{table}
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\end{document}
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