From 7f1327e62e9368981a693c3d90e2e47844b2d8aa Mon Sep 17 00:00:00 2001 From: Arity-T Date: Tue, 15 Oct 2024 20:51:49 +0300 Subject: [PATCH] Results table --- report.tex | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/report.tex b/report.tex index 5c98e31..a57f818 100644 --- a/report.tex +++ b/report.tex @@ -152,6 +152,28 @@ \end{tabularx} \end{table} + \newpage + + \begin{table}[h!] + \centering + \caption{Results obtained in research papers.} + \footnotesize + \begin{tabularx}{\textwidth}{|X|X|X|X|} + \hline + \textbf{Article} & \textbf{Key results} & \textbf{Best algorithms} & \textbf{Metrics} \\ + \hline + Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~\cite{paclitaxel} & Demonstrated that morphological changes in epithelial ovarian cancer (EOC) cells correlate with drug sensitivity, highlighting the potential for monitoring drug resistance. + & Support vector machine (SVM) and neural network (NN) & Accuracy of 94.5\% for SVM and 93.4\% for NN \\ + \hline + Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~\cite{heterogeneity} & Demonstrated a strong correlation between intra-tumor heterogeneity (ITH) and drug resistance in epithelial ovarian cancer (EOC) cells & Least absolute shrinkage and selection operator (LASSO) regression & Area under curve (AUC) of 0.601, 0.594, and 0.589 for 1, 3, and 5 years survival time accordingly \\ + \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} & Proposed a model that incorporates mitochondria-related chemoradiotherapy resistance (MRCRTR) genes. Identified six mitochondria-related genes that affect CRT and the prognosis of esophageal cancer. & Neural network (NN) and least absolute shrinkage and selection operator (LASSO) regression & Root mean squared error (RMSE) of 0.001 for NN and 0.003 for LASSO \\ + \hline + Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} & Developed a novel approach using label-free surface-enhanced Raman spectroscopy (SERS) to profile molecular patterns in the blood of nasopharyngeal cancer (NPC) patients, distinguishing those with radiotherapy sensitivity from those with resistance & Principal component analysis and linear discriminant analysis (PCA-LDA) & Accuracy of 96.7\% for identifying radiotherapy resistance subjects from sensitivity ones and 100\% for identifying the nasopharyngeal cancer (NPC) subjects from healthy ones \\ + \hline + \end{tabularx} + \end{table} + % \section {Первый раздел} % \subsection{Первый подраздел} % Текст первого подраздела