datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf by Jan Tönshoff (10192709)

Table1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.xlsx by Krish Mendapara (18890758)

Image2_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png by Krish Mendapara (18890758)

Image3_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png by Krish Mendapara (18890758)

Image1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png by Krish Mendapara (18890758)

Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX by Zhen Wang (72451)

“…Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. …”

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP by Liangliang Wang (580395)

“…Furthermore, Cox regression analysis revealed 49 DEGs that were associated with survival. Then, 19 DEGs were screened using the LASSO algorithm for IRGs construction and patients were classified into high- and low-risk groups. …”

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. by Linus Woitke (22783534)

“…The image is then cleaned in c) using morphological filtering with an <i>opening</i> operation to remove small-scale noise. …”

An inflammation-associated ferroptosis signature can optimize the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma by Wanyuan Ruan (13763851)

“…Herein, our aim was to identify the inflammation associated ferroptosis (IAF)- biomarkers for contributing the immunotherapy of HCC.</p> <p>Methods: The train cohort from The Cancer Genome Atlas (TCGA) was clustered into three subtypes (C1, C2, and C3) based on the genes related to inflammation and ferroptosis. …”

Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx by Yu Yin (329063)

“…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”

Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf by Yu Yin (329063)

“…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”

Image_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.TIFF by Hao Chen (5190)

“…</p>Conclusion<p>This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.…”

Table_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.XLSX by Hao Chen (5190)

“…</p>Conclusion<p>This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.…”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”

Table_6_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”

Table_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”

Image_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.tif by Ying Zhou (25031)

“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”