Image 7_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…These macrophages exhibited distinct metabolic reprogramming and key immune functions, particularly in crosstalk with T cells and neutrophils.…”

Image 5_MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.jpeg by Yan Jiang (12139)

“…These macrophages exhibited distinct metabolic reprogramming and key immune functions, particularly in crosstalk with T cells and neutrophils.…”

Turkish_native_goat_genotypes by Yalçın YAMAN (20209833)

“…Partial overlap with mixed linear models and genome-wide McNemar tests suggested that both additive and potential nonlinear components contribute to the observed signal.…”

Table 2_Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo.xlsx by Xiaoqing Li (186909)

“…In both the training and validation sets, PTGDS, PNPLA8, and MGLL. …”

Table 1_Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo.xlsx by Xiaoqing Li (186909)

“…In both the training and validation sets, PTGDS, PNPLA8, and MGLL. …”

Table 3_Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo.xlsx by Xiaoqing Li (186909)

“…In both the training and validation sets, PTGDS, PNPLA8, and MGLL. …”

Study flowchart. by Jingqi Dong (22378904)

“…Differential expression gene (DEG) analysis was performed on the profiles, followed by further screening using four machine learning algorithms. Concurrently, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules, and enrichment analysis of WGCNA-derived genes was conducted to explore their biological functions. …”

The top ten related predicted drug compounds. by Jingqi Dong (22378904)

“…Differential expression gene (DEG) analysis was performed on the profiles, followed by further screening using four machine learning algorithms. Concurrently, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules, and enrichment analysis of WGCNA-derived genes was conducted to explore their biological functions. …”

Navigating complex care pathways–healthcare workers’ perspectives on health system barriers for children with tuberculous meningitis in Cape Town, South Africa by Dzunisani Patience Baloyi (19452687)

“…Regular and compulsory training on TB and TBM in children, including continuous mentoring and support to healthcare workers working in child health and TB services in high TB-burden settings, can facilitate early recognition of symptoms and rapid referral for diagnosis. Algorithms outlining referral criteria for patients with possible TBM at both PHC facilities and district level hospitals can guide healthcare providers and facilitate timely referral between different levels of healthcare services. …”

Image 5_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.tif by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Image 1_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.tif by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Image 2_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.tif by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 5_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.xlsx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 7_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.xlsx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 2_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.docx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 4_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.xlsx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 1_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.xlsx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 6_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.xlsx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Image 4_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.tif by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”

Table 3_Identification of ferroptosis-genes associated with pediatric inflammatory bowel disease bioinformatics and machine learning approaches.xlsx by Zhen Xu (92379)

“…</p>Methods<p>RNA-seq data of PIBD from GEO datasets were analyzed using DESeq2, WGCNA, and functional enrichment analysis. Ferroptosis-related diagnostic genes were screened through LASSO, Random Forest, and mSVM-RFE algorithms, and validated in GSE57945 and GSE117993 datasets. …”