Table_2_Prognostic and Predictive Value of BGN in Colon Cancer Outcomes and Response to Immunotherapy.xlsx by Zi-Xuan He (7484534)

“…Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.</p>Results<p>BGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. …”

Table_3_Prognostic and Predictive Value of BGN in Colon Cancer Outcomes and Response to Immunotherapy.xlsx by Zi-Xuan He (7484534)

“…Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.</p>Results<p>BGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. …”

Table_4_Prognostic and Predictive Value of BGN in Colon Cancer Outcomes and Response to Immunotherapy.docx by Zi-Xuan He (7484534)

“…Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.</p>Results<p>BGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. …”

Table_1_Prognostic and Predictive Value of BGN in Colon Cancer Outcomes and Response to Immunotherapy.xlsx by Zi-Xuan He (7484534)

“…Finally, we predicted the immunotherapeutic response rates in the subgroups of low and high BGN expression by TIS score, ImmuCellAI and TIDE algorithms.</p>Results<p>BGN expression demonstrated a statistically significant upregulation in colon cancer tissues than in normal tissues. …”

Table_1_End to end stroke triage using cerebrovascular morphology and machine learning.DOCX by Aditi Deshpande (9591177)

“…Background<p>Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. …”

DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning by Nitin Wankhade (17147465)

“…<p>Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of different systems in the body. ADRA2A regulation is associated with a wide range of effects, including alterations in blood pressure, hypertension, heightened heart rate, etc. …”

DataSheet_1_MX2: Identification and systematic mechanistic analysis of a novel immune-related biomarker for systemic lupus erythematosus.zip by Xiang-Wen Meng (13263633)

“…The expression level of the biomarker was verified by RT‒qPCR. Subsequently, functional enrichment analysis was utilized to identify biomarker-associated pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were applied to calculate the sample immune cell infiltration abundance. …”

Data Sheet 1_Integrative multi-omics identifies MEIS3 as a diagnostic biomarker and immune modulator in hypertrophic cardiomyopathy.docx by Jinchen He (18929662)

“…</p>Conclusion<p>MEIS3 functions as a stromal-centric immunomodulator in HCM, shaping cytokine expression and immune infiltration in the diseased heart. …”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…If you are interested in trying machine learning, the files Features.hdf5 and Target.hdf5 contain the data munged in a convenient form for standard supervised machine learning algorithms.</p><p dir="ltr"><br></p><p dir="ltr">Some large files are in the binary format hdf5 for efficiency in space and read-in. …”

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). …”

Image 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.tif by Haipeng Zhang (3413288)

“…</p>Methods<p>Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. …”

Table 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.docx by Haipeng Zhang (3413288)

“…</p>Methods<p>Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. …”

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx by Fanhai Bu (22315168)

“…Introduction<p>Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). …”