Table 1_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. …”

Image 1_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_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. …”

Table 1_A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients.xlsx 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 3_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 Material for: Development and Validation of a Model Predicting Malignant Potential of Adnexal Masses in Areas with Scarcity of Ultrasound Resources by Cui G. (19564123)

“…Introduction: Appropriately stratifying the risk of adnexal masses is of great importance. Many diagnostic algorithms have been devised, most of which rely on ultrasound features. …”

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)

“…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_PCDHGA10 as a potential prognostic biomarker and correlated with immune infiltration in gastric cancer.tif by Mingyang Zhong (11022996)

“…Additionally, with IHC and mIHC, we applied the machine-learning algorithms to evaluate the localization and expression levels of TILs and immune checkpoints in the tumor microenvironment. …”

Table 1_PCDHGA10 as a potential prognostic biomarker and correlated with immune infiltration in gastric cancer.docx by Mingyang Zhong (11022996)

“…Additionally, with IHC and mIHC, we applied the machine-learning algorithms to evaluate the localization and expression levels of TILs and immune checkpoints in the tumor microenvironment. …”

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

“…Objective<p>The experiment aims to verify the function of Tumor Necrosis Factor Receptor Superfamily Member 19L (RELT) in clear cell renal cell carcinoma (ccRCC).</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)

“…Objective<p>The experiment aims to verify the function of Tumor Necrosis Factor Receptor Superfamily Member 19L (RELT) in clear cell renal cell carcinoma (ccRCC).</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 5_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 4_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 3_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. …”

Table 1_Multi-omics analysis reveals ultraviolet response insights for immunotherapy and prognosis.xlsx 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 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. …”

Image 2_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 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)

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

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

Table 4_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. …”