Image 2_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Data Sheet 4_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”

Image 1_Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.tif by Qing-Lin Ren (22516586)

“…Feature importance was assessed using Shapley Additive Explanations (SHAP). A web-based nomogram was constructed for clinical application.…”

Video 2_Paired reentries maintain ventricular tachycardia: a topological analysis of arrhythmic mechanisms using the index theorem.mp4 by Robin Van Den Abeele (16664361)

“…This ablation strategy consistently terminated all simulations, supporting the applicability of our topology-based approach to VT.</p>Conclusion<p>The index theorem remains valid for scar-related VT. …”

Data Sheet 2_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv by Ge Jin (347352)

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”

Image 3_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.tif by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Supplementary file 1_Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients.doc by Xiaodan Yang (746617)

“…Objective<p>To construct a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients based on multiple machine-learning algorithms, providing decision support to identify high-risk patients and ensure rational clinical medication use.…”

Data Sheet 5_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”

Image 7_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Image 6_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Table 1_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Data Sheet 1_Use of machine learning models to predict mortality in dialysis patients.pdf by Junmin Huang (2431492)

“…This study aimed to systematically develop, compare, and validate 19 machine learning algorithms for predicting all-cause mortality in maintenance hemodialysis patients.…”

Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.…”

Image 5_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…Introduction<p>Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality, characterized by substantial interpatient heterogeneity and limited effective prognostic biomarkers.</p>Methods<p>To address this gap, we constructed a robust prognostic model by integrating over 100 machine learning algorithms—such as LASSO, CoxBoost, and StepCox—based on transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts.…”

Table 1_Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer.xlsx by Lei Shen (76320)

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”

Table 3_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Image 2_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.…”

Video 1_Paired reentries maintain ventricular tachycardia: a topological analysis of arrhythmic mechanisms using the index theorem.mp4 by Robin Van Den Abeele (16664361)

“…This ablation strategy consistently terminated all simulations, supporting the applicability of our topology-based approach to VT.</p>Conclusion<p>The index theorem remains valid for scar-related VT. …”

Data Sheet 1_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…The risk score model constructed based on these biomarkers could effectively predict the survival rate of cervical cancer patients (AUC >0.6 for 1/3/5 years). …”

Data Sheet 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung ad... by Lian-jie Ruan (22327876)

“…This study aims to develop a risk model to guide treatment for these patients.</p>Materials and methods<p>RNA-seq data from TCGA and GEO were analyzed using Cox regression and 10 machine learning algorithms to identify prognostic genes and stratify patients. …”