Table 2_Multi-omics reveals efferocytosis-related hub genes as biomarkers for ustekinumab response in colitis.csv by Jun-meng Wang (9959245)

“…Machine learning algorithms screened hub genes, followed by molecular docking to assess interactions with UST. …”

Image 5_Multi-omics reveals efferocytosis-related hub genes as biomarkers for ustekinumab response in colitis.tif by Jun-meng Wang (9959245)

“…Machine learning algorithms screened hub genes, followed by molecular docking to assess interactions with UST. …”

Image 5_High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.png by Erin McDonald (3535409)

“…</p>Methods<p>This pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. Our analysis uses machine learning and statistical algorithms to perform unsupervised clustering, multiple visualizations, and statistical analysis of high parameter flow cytometry data. …”

Image 6_Multi-omics reveals efferocytosis-related hub genes as biomarkers for ustekinumab response in colitis.pdf by Jun-meng Wang (9959245)

“…Machine learning algorithms screened hub genes, followed by molecular docking to assess interactions with UST. …”

Image 2_High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.png by Erin McDonald (3535409)

“…</p>Methods<p>This pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. Our analysis uses machine learning and statistical algorithms to perform unsupervised clustering, multiple visualizations, and statistical analysis of high parameter flow cytometry data. …”

Table 1_Multi-omics reveals efferocytosis-related hub genes as biomarkers for ustekinumab response in colitis.csv by Jun-meng Wang (9959245)

“…Machine learning algorithms screened hub genes, followed by molecular docking to assess interactions with UST. …”

Table 1_High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.docx by Erin McDonald (3535409)

“…</p>Methods<p>This pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. Our analysis uses machine learning and statistical algorithms to perform unsupervised clustering, multiple visualizations, and statistical analysis of high parameter flow cytometry data. …”

Image 2_Multi-omics reveals efferocytosis-related hub genes as biomarkers for ustekinumab response in colitis.pdf by Jun-meng Wang (9959245)

“…Machine learning algorithms screened hub genes, followed by molecular docking to assess interactions with UST. …”

Integrating urinary metabolomics and clinical datasets for multi-cancer detection by Dongyong Lee (18786694)

“…</p><p><br></p><p dir="ltr">## Data format</p><p><br></p><p dir="ltr">- Each CSV file contains **two columns** without a header:</p><p dir="ltr"> 1. …”

Table 3_Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.xlsx by Maoqiang Lin (17046318)

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”

Table 4_Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.xlsx by Maoqiang Lin (17046318)

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”

Table 2_Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.xlsx by Maoqiang Lin (17046318)

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”

Table 1_Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.xlsx by Maoqiang Lin (17046318)

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”

Table 1_A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery.xlsx by Su-Zhen Yang (21448877)

“…Eleven key features were identified through group comparisons and used for model development. Twenty machine learning algorithms were evaluated, and the top five algorithms were used to build the final model by stacking.…”

Table 2_A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery.xlsx by Su-Zhen Yang (21448877)

“…Eleven key features were identified through group comparisons and used for model development. Twenty machine learning algorithms were evaluated, and the top five algorithms were used to build the final model by stacking.…”

Supplementary Material for: Assessing the accuracy of the international evidence-based Kyoto guidelines for detecting malignancy in intraductal papillary mucinous neoplasms of the... by figshare admin karger (2628495)

“…Introduction Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic tumours with an associated risk of malignant transformation. Due to the widespread use of imaging techniques, the diagnosis of IPMNs has been rising. …”

Table 1_Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning.xlsx by Tairan Hu (21086501)

“…Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.…”

Image 2_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Table 1_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.docx by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation by Zhen Chen (129176)

“…PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. ROC analysis evaluated their diagnostic performance. …”