Image 1_Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.pdf حسب Jihai Zhou (1876561)

"…</p>Methods<p>Data samples from the Gene Expression Omnibus database and protein quantitative trait loci data from genome-wide association studies were integrated to identify overlapping genes. Three machine learning (ML) algorithms were employed to screen core hub genes from these overlapping genes, followed by the construction and external validation of a diagnostic model. …"

Data Sheet 1_Multi-omics integration analysis based on plasma circulating proteins reveals potential therapeutic targets for ulcerative colitis.xlsx حسب Jihai Zhou (1876561)

"…</p>Methods<p>Data samples from the Gene Expression Omnibus database and protein quantitative trait loci data from genome-wide association studies were integrated to identify overlapping genes. Three machine learning (ML) algorithms were employed to screen core hub genes from these overlapping genes, followed by the construction and external validation of a diagnostic model. …"

Table 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Image 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Image 4_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Table 1_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Image 1_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Table 2_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xls حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Table 4_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

Image 2_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Differentially expressed AICD-related genes were identified through differential expression analysis, univariate Cox regression, and machine learning algorithms (LASSO, random forest, and CoxBoost). …"

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... حسب Zhaokai Zhou (15239078)

"…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. 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... حسب Zhaokai Zhou (15239078)

"…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. 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... حسب Zhaokai Zhou (15239078)

"…Next, we comprehensively explored the distinct heterogeneity and characteristics for four CAFs-based BLCA subtypes. Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…"

Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer حسب Yanliang Chen (15235486)

"…</p> <p>Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (<i>APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2</i> and <i>TSPAN2</i>) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. …"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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). …"