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)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Image 1_Triglyceride–glucose index as a key predictor of ARDS in acute pancreatitis: SHAP analysis reveals its critical role in risk stratification.pdf by Ju Luo (3233037)

“…The SHAP method further confirmed that the TyG index serves as a significant predictor for the development of ARDS in patients with acute pancreatitis.…”

Image 1_Immune-related RELT drives clear cell renal cell carcinoma progression through JAK/STAT signaling pathway activation.tiff by Yini Wang (410389)

“…</p>Methods<p>The relationship between differential expression of RELT in ccRCC and clinical prognosis was investigated based on data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) databases. …”

Image 2_Immune-related RELT drives clear cell renal cell carcinoma progression through JAK/STAT signaling pathway activation.tiff by Yini Wang (410389)

“…</p>Methods<p>The relationship between differential expression of RELT in ccRCC and clinical prognosis was investigated based on data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) databases. …”

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

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”

Table 3_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.xlsx by Yi Li (1144)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Table 1_Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance.xlsx by Jian Feng (317890)

“…</p>Conclusion<p>This study developed a reliable prognostic signature based on nine CRPGs for predicting LUAD patient outcomes. …”

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

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”

Image 2_Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance.jpeg by Jian Feng (317890)

“…</p>Conclusion<p>This study developed a reliable prognostic signature based on nine CRPGs for predicting LUAD patient outcomes. …”

Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing by Qunita Brown (19751520)

“…High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. …”

Data Sheet 2_A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients.csv by Jun Mou (4113313)

“…The immune cell composition and tumor microenvironment (TME) characteristics were assessed using ESTIMATE, MCPcounter, and CIBERSORT algorithms. Based on prognostic DEGs, we constructed a four-gene prognostic signature (MAOB, IGFBP2, SERPINA1, and LGR6).…”

Supplementary file 1_Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy.xlsx by Feng Zhang (6548)

“…A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313).…”

Key predictors of Gini importance. by Ayşe Banu Birlik (21587723)

“…Traditional models, such as EuroSCORE I, often struggle to capture the complex interactions among clinical variables, leading to suboptimal performance in specific populations. In this study, we developed and validated the Ensemble-Based Risk Estimation System (ERES), a machine learning model designed to enhance mortality prediction in patients undergoing coronary artery bypass grafting and/or valve surgery. …”

Table 4_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)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Image 3_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)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Image 1_Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance.jpeg by Jian Feng (317890)

“…</p>Conclusion<p>This study developed a reliable prognostic signature based on nine CRPGs for predicting LUAD patient outcomes. …”

Table 2_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)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Data Sheet 1_A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients.csv by Jun Mou (4113313)

“…The immune cell composition and tumor microenvironment (TME) characteristics were assessed using ESTIMATE, MCPcounter, and CIBERSORT algorithms. Based on prognostic DEGs, we constructed a four-gene prognostic signature (MAOB, IGFBP2, SERPINA1, and LGR6).…”

Image 1_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)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…The DEGs were filtered based on a log2 fold change threshold of ≥2.</p><p dir="ltr">(C) Pathway-based annotation of DEGs via KEGG enrichment analysis. …”