Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx by Fanhai Bu (22315168)

“…Introduction<p>Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). …”

Image 13_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Table_1_Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.docx by Tao Xiong (287683)

“…First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. …”

Image 1_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.jpeg by Ruoya Wang (842048)

“…Additionally, we analyzed the immune microenvironment and enriched pathways across different subtypes using multiple algorithms. Finally, the “oncoPredict” R package was used to assess potential drug sensitivities in high-risk and low-risk groups.…”

Table 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx by Peize Yu (21837977)

“…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). A prognostic risk model was developed via multivariate Cox regression. …”

Table 1_Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait... by Guoqing Liu (93712)

“…A prediction model was constructed based on three hub neutrophil coexpressed genes in AMI, and the results revealed good accuracy. …”

Image 4_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif by Peize Yu (21837977)

“…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). A prognostic risk model was developed via multivariate Cox regression. …”

Supplementary file 2_The role of α-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learni... by Qing Lu (28914)

“…Sepsis-related targets were obtained from the GEO dataset GSE26440, and the intersection of these datasets was analyzed to reveal common targets. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (L1-LASSO, RF, and SVM) were applied to identify biomarkers. …”

Data Sheet 1_Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative... by Guoqing Liu (93712)

“…A prediction model was constructed based on three hub neutrophil coexpressed genes in AMI, and the results revealed good accuracy. …”

Table_2_Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.docx by Tao Xiong (287683)

“…First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. …”

Image 5_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 10_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 8_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Table_3_Association between frontal fibrosing Alopecia and Rosacea: Results from clinical observational studies and gene expression profiles.docx by Lin Liu (74495)

“…Later, we conducted a functional enrichment analysis and protein-protein interaction network and used seven algorithms to identify hub genes. …”

Table_5_Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.docx by Tao Xiong (287683)

“…First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. …”

Table 4_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Data_Sheet_1_Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.docx by Tao Xiong (287683)

“…First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. …”

Image 11_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Table 2_Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait... by Guoqing Liu (93712)

“…A prediction model was constructed based on three hub neutrophil coexpressed genes in AMI, and the results revealed good accuracy. …”