From 49533060876111224f59b50a806366bda77b123f Mon Sep 17 00:00:00 2001 From: Arity-T Date: Sun, 3 Nov 2024 20:15:53 +0300 Subject: [PATCH] =?UTF-8?q?=D0=94=D0=BE=D0=B1=D0=B0=D0=B2=D0=B8=D0=BB=20gl?= =?UTF-8?q?ut=20=D0=B2=20=D1=82=D0=B0=D0=B1=D0=BB=D0=B8=D1=86=D1=83=20?= =?UTF-8?q?=D1=81=20=D0=B0=D0=BB=D0=B3=D0=BE=D1=80=D0=B8=D1=82=D0=BC=D0=B0?= =?UTF-8?q?=D0=BC=D0=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml_table/ml_table.tex | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/ml_table/ml_table.tex b/ml_table/ml_table.tex index b96645e..ae8a11c 100644 --- a/ml_table/ml_table.tex +++ b/ml_table/ml_table.tex @@ -18,21 +18,24 @@ \centering \caption*{Table 2. Machine learning algorithms comparison.} \footnotesize - \begin{tabularx}{\textwidth}{|p{6cm}|X|X|X|X|X|X|X|X|X|X|} + \begin{tabularx}{\textwidth}{|p{9cm}|X|X|X|X|X|X|X|X|X|X|X|X|X|X|} \hline - \textbf{Article} & \textbf{DT} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} & \textbf{LR} \\ + \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} + & \textbf{SuperPC} & \textbf{Enet} & \textbf{GBM}\\ \hline - Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~[1] & + & & + & + & + & & & & & \\ + Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~[1] & + & + & + & + & & & & & & & & & & \\ \hline - Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~[2] & & & & & + & & & & & \\ + Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~[2] & & & & & + & & & & & & & & & \\ \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~[3] & & + & + & + & + & + & & + & + & \\ + 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] & & + & + & + & + & + & & + & + & & & & & \\ \hline - Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~[4] & & & & & & & + & & & \\ + Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~[4] & & & & & & & + & & & & & & & \\ \hline - A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~[5] & & & & & + & & & & & + \\ + A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~[5] & & & & & + & & & & & + & & & & \\ \hline - Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~[6] & + & + & + & & & + & & & & \\ + Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~[6] & + & + & + & & & + & & & & & & & & \\ + \hline + Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance~[7] & & & + & & + & & & & & & + & + & + & + \\ \hline \end{tabularx} \end{table}