Table 4_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 1_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Image 1_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 7_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 10_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Image 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 5_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Image 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 6_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Example of how the Resnet34UNet model functions. by Henrik Mustonen (20378529)

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. by Hiroyuki Ichijo (13891153)

Schematic of the mean-field Ising simulation evolving in time. by Rostam M. Razban (22232522)

Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<p>Among 46 DEGs whose expression significantly changed in A549 and BEAS-2B cell lines, the core genes were selected using three algorithms: MCC, MNC, and DEGREE. <b>(A)</b> The top 10 genes were prioritized using MCC analysis, which identifies the largest clique within the PPI network and selects the central proteins within the clique. …”

Heatmap Comparison. by Pingping Yan (462509)

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Simulation results of sudden reduction of load torque. by Haoran Cui (7235234)

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Sensitivity analysis of parameters <i>α</i> and <i>β</i>. by Haoran Cui (7235234)

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AP improvement rates for various targets on the RSOD dataset. by Pingping Yan (462509)

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RFAConv working principle. by Pingping Yan (462509)

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