Image 2_Unraveling the role of histone acetylation in sepsis biomarker discovery.tif حسب Feng Cheng (124653)

"…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …"

Table 4_Unraveling the role of histone acetylation in sepsis biomarker discovery.xlsx حسب Feng Cheng (124653)

"…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …"

Image 1_Unraveling the role of histone acetylation in sepsis biomarker discovery.tif حسب Feng Cheng (124653)

"…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …"

Table 5_Unraveling the role of histone acetylation in sepsis biomarker discovery.csv حسب Feng Cheng (124653)

"…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …"

Data Sheet 1_Identification of key ferroptosis-related genes and therapeutic target in nasopharyngeal carcinoma.zip حسب Yuanyuan Gu (3813658)

"…Four machine learning algorithms screened hub genes, validated by ROC curves. …"

Table 1_Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis.docx حسب Chengyuan Xu (4174936)

"…</p>Methods<p>We constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). …"

Image 2_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif حسب Xudong Shen (205653)

"…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …"

Image 3_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif حسب Xudong Shen (205653)

"…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …"

Image 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif حسب Xudong Shen (205653)

"…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …"

Table 2_Multi-omics dissection of fatty acid metabolism heterogeneity identifies PRDX1 as a prognostic marker in bladder cancer.xlsx حسب Li Wang (15202)

"…Cross−platform scoring and co−expression analysis produced a refined high−FAM gene set. …"

Image 4_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif حسب Xudong Shen (205653)

"…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …"

Integrative analysis of mitochondrial and immune pathways in diabetic kidney disease: identification of AASS and CASP3 as key predictors and therapeutic targets حسب Xinxin Yu (528120)

"…Machine learning algorithms were employed to prioritize key biomarkers for further investigation. …"

Table 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.docx حسب Xudong Shen (205653)

"…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …"

Image 5_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif حسب Xudong Shen (205653)

"…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …"

Table 1_Multi-omics dissection of fatty acid metabolism heterogeneity identifies PRDX1 as a prognostic marker in bladder cancer.docx حسب Li Wang (15202)

"…Cross−platform scoring and co−expression analysis produced a refined high−FAM gene set. …"

Image 3_Development of a machine learning-derived dendritic cell signature for prognostic stratification in lung adenocarcinoma.tif حسب Fang Zhou (43765)

"…High-dimensional weighted gene co-expression network analysis (hdWGCNA) and pseudotime analysis were utilized to identify functional modules and lineage trajectories. …"

Image 2_Development of a machine learning-derived dendritic cell signature for prognostic stratification in lung adenocarcinoma.tif حسب Fang Zhou (43765)

"…High-dimensional weighted gene co-expression network analysis (hdWGCNA) and pseudotime analysis were utilized to identify functional modules and lineage trajectories. …"

Image 1_Development of a machine learning-derived dendritic cell signature for prognostic stratification in lung adenocarcinoma.tif حسب Fang Zhou (43765)

"…High-dimensional weighted gene co-expression network analysis (hdWGCNA) and pseudotime analysis were utilized to identify functional modules and lineage trajectories. …"

Table_1_Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning.docx حسب Wenhui Chen (3208248)

"…By analyzing differentially expressed genes (DEGs) and module genes using weighted gene co-expression networks (WGCNA), functional enrichment analysis, and three machine learning algorithms, we identified twelve diseases shared genes, and two diagnostic genes, including GLIPR1 and MAMLD1. …"

Table_1_Shared diagnostic genes and potential mechanism between PCOS and recurrent implantation failure revealed by integrated transcriptomic analysis and machine learning.docx حسب Wenhui Chen (3208248)

"…By analyzing differentially expressed genes (DEGs) and module genes using weighted gene co-expression networks (WGCNA), functional enrichment analysis, and three machine learning algorithms, we identified twelve diseases shared genes, and two diagnostic genes, including GLIPR1 and MAMLD1. …"