Identification of potential circadian rhythm-related hub genes and immune infiltration in preeclampsia through bioinformatics analysis by Juan Tang (437969)

“…Molecular subtyping based on their expression revealed two PE subtypes with distinct immune infiltration patterns and biological functions. Regulatory network construction highlighted potential upstream mechanisms.…”

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. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

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. …”

Image 3_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. …”