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)

“…Thus, the aim of this study was to highlight prevention and early detection opportunities in high-risk populations by identifying common biomarkers for T1DM and ccRCC.</p>Methods<p>Based on multiple publicly available datasets, WGCNA was applied to identify gene modules closely associated with T1DM, which were then integrated with prognostic DEGs in ccRCC. …”

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)

“…Thus, the aim of this study was to highlight prevention and early detection opportunities in high-risk populations by identifying common biomarkers for T1DM and ccRCC.</p>Methods<p>Based on multiple publicly available datasets, WGCNA was applied to identify gene modules closely associated with T1DM, which were then integrated with prognostic DEGs in ccRCC. …”

Data Sheet 1_Development and validation of an interpretable machine learning model for acute radiation dermatitis in breast cancer.pdf by Xuejuan Duan (15892880)

“…Fourteen machine learning algorithms were evaluated via 10-fold cross-validation, with model selection based on Area Under the Curve (AUC) and other metrics. …”

Image 5_Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer.tif by Lin Ni (5337725)

“…Three different machine learning algorithms were used to screen out the characteristic circadian genes associated with BC prognosis. …”

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. …”

Data Sheet 1_Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.docx by Shuyu Wen (15411107)

“…This study aims to employ machine learning algorithms to establish a practical platform for the prediction of reintubation.…”

Table 1_Integrative analysis of semaphorins family genes in colorectal cancer: implications for prognosis and immunotherapy.docx by Jiahao Zhu (7817738)

“…However, the prognostic value of SEMA-related genes in colorectal cancer (CRC) remains unclear.</p>Methods<p>We applied a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a SEMAs-related score (SRS). …”

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)

“…Thus, the aim of this study was to highlight prevention and early detection opportunities in high-risk populations by identifying common biomarkers for T1DM and ccRCC.</p>Methods<p>Based on multiple publicly available datasets, WGCNA was applied to identify gene modules closely associated with T1DM, which were then integrated with prognostic DEGs in ccRCC. …”

Image 1_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Intercellular communication was analyzed using CellChat, while machine learning, incorporating seven different algorithms, was applied to identify key regulatory genes.…”

Table 2_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.xlsx by Zhi-Chuan He (21563657)

“…This study aimed to identify T cell-related signatures for TNBC diagnosis and prognosis.</p>Methods<p>Clinical data and transcriptomic profiles were obtained from the TCGA-BRCA dataset, and single-cell RNA sequencing (scRNA-seq) data were downloaded from the GEO database. …”

Data Sheet 1_Exploring shared biomarkers and shared pathways in insomnia and atherosclerosis using integrated bioinformatics analysis.docx by Qichong Yang (19816146)

“…Our study aimed to explore the shared pathways and diagnostic biomarkers of ISM-related AS using integrated bioinformatics analysis.</p>Methods<p>We download the datasets from the Gene Expression Omnibus database and the GeneCards database. …”

Image 1_Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.tif by Shuyu Wen (15411107)

“…This study aims to employ machine learning algorithms to establish a practical platform for the prediction of reintubation.…”

Image 2_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…This study aimed to identify T cell-related signatures for TNBC diagnosis and prognosis.</p>Methods<p>Clinical data and transcriptomic profiles were obtained from the TCGA-BRCA dataset, and single-cell RNA sequencing (scRNA-seq) data were downloaded from the GEO database. …”

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).…”

Data Sheet 1_Multi-omics analysis reveals ultraviolet response insights for immunotherapy and prognosis.csv by DanHua Zhang (22315099)

“…Key genes (Hub-UVR.Sig) were identified via six machine learning algorithms, and breast cancer (BRCA) subtypes were classified through consensus clustering. …”

Image 6_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Intercellular communication was analyzed using CellChat, while machine learning, incorporating seven different algorithms, was applied to identify key regulatory genes.…”

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)

“…Thus, the aim of this study was to highlight prevention and early detection opportunities in high-risk populations by identifying common biomarkers for T1DM and ccRCC.</p>Methods<p>Based on multiple publicly available datasets, WGCNA was applied to identify gene modules closely associated with T1DM, which were then integrated with prognostic DEGs in ccRCC. …”

Table 1_Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest.... by Qiang Wu (31071)

“…Objective<p>In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.…”

An open-pit mine segmentation dataset for deep learning by Lin Gang (18600526)

“…It was developed through a systematic process. Firstly, by conducting comprehensive literature research, the Point of Interest (POI) data of open-pit mines was summarized. …”

Table 1_Using machine learning to predict the rupture risk of multiple intracranial aneurysms.xlsx by Junqiang Feng (10300150)

“…By analyzing detailed morphological and anatomical parameters, our model provides a tailored approach to rupture risk assessment in MIAs, offering potential improvements over existing methods.</p>Methods<p>To address dataset imbalance, we conducted five-fold cross-validation. …”