DataSheet1_The evolution of flexibility and function in the Fc domains of IgM, IgY, and IgE.pdf by Rosaleen A. Calvert (10039787)

“…</p>Results and discussion<p>We analysed the scattering curves in terms of contributions from a pool of variously bent models chosen by a non-negative linear least-squares algorithm and found that the four proteins form a series in which the proportion of acutely bent material increases: IgM-Fc < IgY-Fc < plIgE-Fc < huIgE-Fc. …”

Image 1_Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.pdf by Jihai Zhou (1876561)

“…Additionally, immune infiltration, functional enrichment, and regulatory networks were analyzed. …”

Correlation of <i>VDAC1</i>, <i>HSPA8</i>, <i>SOD1</i>, <i>HSPA9</i>, <i>TXN</i>, and <i>SNCA</i> with immune cell infiltration in sepsis-induced ALI. by Xue Fu (549661)

Antibody pair dataset characteristics. by Katharina Waury (14203284)

Table 1_Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.docx by Hong Chen (108084)

“…Introduction<p>Chronic kidney disease (CKD) poses a significant global health challenge, requiring timely interventions to manage renal function decline. Traditional predictive models often lack accuracy and generalizability. …”

Data Sheet 1_Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.xlsx by Jihai Zhou (1876561)

“…Additionally, immune infiltration, functional enrichment, and regulatory networks were analyzed. …”

<b>dGenhancer v2</b>: A software tool for designing oligonucleotides that can trigger gene-specific Enhancement of Protein Translation. by Adam Master (20316450)

“…<p dir="ltr"> Software tool designed to calculate 5’UTR properties, enabling the creation of oligonucleotides that can trigger gene-specific enhancement of protein translation.<br> An excel-based calculator - dGenhancer can be used to search for putative 5’UTR cis-acting elements, which functional activity could be determined by Gibbs energy-dependent secondary structure formation. …”

Changed differential alternative splicing events (DASEs) patterns between PCOS and Normal granulosa cells. by Linlin Yang (737661)

Table 8_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 9_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 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. …”