Presentation 1_A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study.pptx by Sheng Cheng (202385)

“…</p>Methods<p>A total of 200 TANU in 200 patients were enrolled. …”

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. …”

Image 2_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_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. …”

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).…”

Table 2_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

“…LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”

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

“…Objective<p>This study aimed to leverage bioinformatics approaches to identify novel biomarkers and characterize the molecular mechanisms underlying hypertrophic cardiomyopathy (HCM).</p>Methods<p>Two RNA-sequencing datasets (GSE230585 and GSE249925) were obtained from the Gene Expression Omnibus (GEO) repository. …”

Table 4_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

“…LASSO regression identified 25 significant features for the models. Among the six algorithms tested, the radial basis function support vector machine (RBF-SVM) had the highest AUC at 0.771. …”

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. …”