44 lines
2.9 KiB
TeX
44 lines
2.9 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*{\small Table 2. Machine learning algorithms comparison. Algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Linear Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).}
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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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