DataSheet2_Identifying target ion channel-related genes to construct a diagnosis model for insulinoma.ZIP by Shuangyang Mo (14859565)

“…<p>Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. …”

Table 2_Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorecta... by Tianle Xue (21462608)

“…In addition, immune cell infiltration, protein–protein interaction (PPI) networks, and drug enrichment analyses—including molecular docking—were performed to further elucidate the biological functions and therapeutic potentials of the identified genes.…”

Data_Sheet_1_Comprehensive analysis of the diagnostic and therapeutic value, immune infiltration, and drug treatment mechanisms of GTSE1 in lung adenocarcinoma.docx by Guanqiang Yan (18472116)

“…GTSE1 expression emerged as an independent predictive factor in both univariate and multivariate Cox regression analyses. Furthermore, functional enrichment analysis suggested a potential association between GTSE1 and the cell cycle, p53 signaling pathway, as well as ubiquitin-mediated protein degradation. …”

Image_4_Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment.tif by Xiuqin Lu (11401169)

“…The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. …”

Image_2_Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment.tif by Xiuqin Lu (11401169)

“…The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. …”

Image_1_Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment.tif by Xiuqin Lu (11401169)

“…The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. …”

Table 3_Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorecta... by Tianle Xue (21462608)

“…In addition, immune cell infiltration, protein–protein interaction (PPI) networks, and drug enrichment analyses—including molecular docking—were performed to further elucidate the biological functions and therapeutic potentials of the identified genes.…”

DataSheet1_Identification and Verification of Potential Hub Genes in Amphetamine-Type Stimulant (ATS) and Opioid Dependence by Bioinformatic Analysis.doc by Wei Zhang (405)

“…GEO2R and Venn diagram were performed to identify differentially expressed genes (DEGs) and coexpressive DEGs (CDEGs). Functional annotation and protein–protein interaction network detected the potential functions. …”

DataSheet_1_Pan-cancer analysis reveals that G6PD is a prognostic biomarker and therapeutic target for a variety of cancers.zip by Tao Zeng (103451)

“…In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. …”

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. …”

DataSheet_1_Identification of novel molecular subtypes and a signature to predict prognosis and therapeutic response based on cuproptosis-related genes in prostate cancer.zip by Jili Zhang (7399961)

“…Furthermore, 4D Label-Free LC-MS/MS and RNAseq were used to investigate the changes in CRGs at protein and RNA levels after the knockdown of the key model gene B4GALNT4.…”

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. …”

Data Sheet 1_Diagnostic lncRNA biomarkers and immune-related ceRNA networks for osteonecrosis of the femoral head in metabolic syndrome identified by plasma RNA sequencing and mach... by Haoyan Sun (22172911)

“…The MetS dataset from the Gene Expression Omnibus (GEO) was integrated, and weighted gene co-expression network analysis (WGCNA), functional enrichment, protein-protein interaction (PPI) network analysis, MCODE, CytoHubba-MCC, and random forest (RF) algorithms were employed to identify hub mRNAs and their associated lncRNAs. …”

DataSheet_1_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. …”

Supplementary file 1_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. …”

Table_1_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_2_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_4_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. …”

DataSheet_2_Identification of novel molecular subtypes and a signature to predict prognosis and therapeutic response based on cuproptosis-related genes in prostate cancer.zip by Jili Zhang (7399961)

“…Furthermore, 4D Label-Free LC-MS/MS and RNAseq were used to investigate the changes in CRGs at protein and RNA levels after the knockdown of the key model gene B4GALNT4.…”

Image4_Construction of a mitochondrial dysfunction related signature of diagnosed model to obstructive sleep apnea.TIF by Qian Liu (135614)

“…A protein–protein interaction network of the DEGs between the mitochondrial dysfunction-related clusters was constructed using STRING database and the hub genes were identified. …”