Image 1_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

Image 2_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

Image 4_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

Image 1_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.jpeg by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 1_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.docx by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 2_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.docx by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 1_Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.docx by Chen Lin (95910)

“…Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.…”

Data Sheet 3_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 2_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Table 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.xlsx by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 4_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 5_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Supplementary file 1_Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy.xlsx by Xueming Ma (2119150)

“…</p>Methods<p>Multi-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. A variety of algorithms and analysis techniques, such as Kaplan-Meier analysis, Cox regression analysis, and ConsensusClusterPlus clustering analysis, were used to identify PRGs related to the prognosis of bladder cancer patients, and functional analysis and clustering analysis were conducted in depth. …”

Data Sheet 1_Identification and validation of cellular senescence-related genes and immune cell infiltration characteristics in intervertebral disc degeneration.pdf by Hao Li (31608)

“…A protein–protein interaction (PPI) network was also drawn, and the hub SRDEGs were obtained using 11 different algorithms. Immune infiltration analysis was then performed. …”

Image 1_Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis.pdf by Cheng Pu (14791088)

“…Furthermore, we conducted molecular subtyping, qRT-PCR, biological functions, immune infiltration, drug sensitivity, and single cell analyses on EIGPS.…”

Data Sheet 1_Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis.xlsx by Cheng Pu (14791088)

“…Furthermore, we conducted molecular subtyping, qRT-PCR, biological functions, immune infiltration, drug sensitivity, and single cell analyses on EIGPS.…”

Table 8_Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.docx by Dong Pan (1835707)

“…A STAT3 predictive model was developed using six machine learning algorithms. Model performance was assessed using receiver operating characteristic (ROC) and related diagnostic statistical indicators.…”

Image 10_Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.tif by Dong Pan (1835707)

“…A STAT3 predictive model was developed using six machine learning algorithms. Model performance was assessed using receiver operating characteristic (ROC) and related diagnostic statistical indicators.…”

Table 2_Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.xlsx by Dong Pan (1835707)

“…A STAT3 predictive model was developed using six machine learning algorithms. Model performance was assessed using receiver operating characteristic (ROC) and related diagnostic statistical indicators.…”