Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.…”

Table 7_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.…”

Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.…”

Table 10_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 4_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table2_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table3_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table4_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table1_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table6_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Table5_Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine.xlsx by KeShangJing Wu (19931535)

“…To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.…”

Assessing the risk of acute kidney injury associated with a four-drug regimen for heart failure: a ten-year real-world pharmacovigilance analysis based on FAERS events by Sen Lin (182597)

“…Disproportionality analysis and subgroup analysis were performed using four algorithms. Time-to-onset (TTO) analysis was used to assess the temporal risk patterns of ADE occurrence. …”

<b>Leveraging protected areas for dual goals of biodiversity conservation and zoonotic</b> <b>risk reduction</b> by Li Yang (13558573)

“…Each approach was run using both the Additive Benefit Function (ABF) and Core-Area Zonation (CAZ) algorithms.…”

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.…”

Data Sheet 1_Differential neuropilin isoform expressions highlight plasticity in macrophages in the heterogenous TME through in-silico profiling.docx by Hyun-Jee Han (20858765)

“…Datasets were processed using established bioinformatics pipelines, including clustering algorithms, to determine cellular heterogeneity and quantify NRP isoform expression within distinct macrophage populations. …”

Data Sheet 2_Differential neuropilin isoform expressions highlight plasticity in macrophages in the heterogenous TME through in-silico profiling.docx by Hyun-Jee Han (20858765)

“…Datasets were processed using established bioinformatics pipelines, including clustering algorithms, to determine cellular heterogeneity and quantify NRP isoform expression within distinct macrophage populations. …”

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). …”

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…., 2022a), to estimate the spatiotemporal dynamics of SOC in different soil layers and further evaluate the impacts of different climate response functions on SOC estimates in the Qinling Mountains. …”

Table 5_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…To evaluate MMPred’s ability to produce biologically meaningful insights, we conducted a comprehensive assessment involving i) predicting associations between known HLA class II human autoepitopes and microbial-peptide mimicry, ii) interpreting these predictions within a systems biology framework to identify potential functional links between the predicted autoantigens and pathophysiological pathways related to autoimmune diseases, and iii) analyzing illustrative cases in the context of SARS-CoV-2 infection and autoimmunity. …”