Image 7_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Table 2_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xls 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. …”

Image 3_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.jpeg by Ruoya Wang (842048)

“…This study aimed to determine whether polyamine metabolism-related genes (PMRGs) could predict prognosis and immunotherapy efficacy in Breast Cancer (BC).</p>Methods<p>We conducted a comprehensive multi-omics analysis of PMRG expression profiles in BC. …”

Table 4_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. …”

Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Post-translational modifications (PTMs) regulate immune signaling and tumor behavior, yet PTM-informed biomarkers for ccRCC remain underexplored.</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Image 2_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. …”

Table 2_Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer.xlsx by Ruoya Wang (842048)

“…This study aimed to determine whether polyamine metabolism-related genes (PMRGs) could predict prognosis and immunotherapy efficacy in Breast Cancer (BC).</p>Methods<p>We conducted a comprehensive multi-omics analysis of PMRG expression profiles in BC. …”

Data Sheet 1_Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data.docx by Renjie Lu (2021845)

“…This research aimed to create and assess a predictive model for the risk of sarcopenia in CKD patients, utilizing data obtained from the China Health and Retirement Longitudinal Study (CHARLS).</p>Methods<p>Sarcopenia was diagnosed based on the Asian Working Group for Sarcopenia (AWGS 2019) criteria, including low muscle strength, reduced physical performance, and low muscle mass. …”

Image 1_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…</p>Conclusion<p>The IERGs-based signature offers reliable prognostication for GBM, validated across multiple datasets. …”

Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Post-translational modifications (PTMs) regulate immune signaling and tumor behavior, yet PTM-informed biomarkers for ccRCC remain underexplored.</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Table 1_CEACAM6 as a machine learning derived immune biomarker for predicting neoadjuvant chemotherapy response in HR+/HER2− breast cancer.xlsx by Dalang Fang (22130155)

“…Overlapping DEGs were further screened using LASSO, random forest, and SVM-RFE algorithms. Predictive models were constructed with 10 machine learning algorithms and interpreted using SHAP. …”

KS statistic plot in predicting LAT/SEC risk. by Chaoqun Huang (529718)

“…This study aimed to construct and validate an interpretable predictive model of LAT/SEC risk in NVAF patients using machine learning (ML) methods.</p><p>Methods</p><p>Electronic medical records (EMR) data of consecutive NVAF patients scheduled for catheter ablation at the First Hospital of Jilin University from October 1, 2022, to February 1, 2024, were analyzed. …”

Multivariate logistic regression analysis. by Chaoqun Huang (529718)

“…This study aimed to construct and validate an interpretable predictive model of LAT/SEC risk in NVAF patients using machine learning (ML) methods.</p><p>Methods</p><p>Electronic medical records (EMR) data of consecutive NVAF patients scheduled for catheter ablation at the First Hospital of Jilin University from October 1, 2022, to February 1, 2024, were analyzed. …”

Image 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 8_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 2_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 2_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…”

Table 4_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…”

Table 1_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”