Glucocorticoid related genes. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

CIBERSORTx results. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Differential gene expression analysis results. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

GSEA result. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Enrichment analysis of GO. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Lasso gene RF hub gene. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Enrichment analysis of KEGG. by Yinghao Ren (17915291)

“…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”

Image 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…Differentially altered pathways were evaluated by using the enrich plot package in R for visualization of functional enrichment (i.e., dot plot).</p>…”

DataSheet1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.docx by Yutong Fang (16621143)

“…Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. …”

Table1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.xlsx by Yutong Fang (16621143)

“…Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. …”

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). …”