Image 10_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in pre... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 2_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 7_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Data Sheet 1_Resveratrol contributes to NK cell-mediated breast cancer cytotoxicity by upregulating ULBP2 through miR-17-5p downmodulation and activation of MINK1/JNK/c-Jun signali... by Bisha Ding (5803799)

“…UL16-binding protein 2 (ULBP2), always expressed or elevated on cancer cells, functions as a key NKG2D ligand. ULBP2-NKG2D ligation initiates NK cell activation and subsequent targeted elimination of cancer cells. …”

Data from "Minigene splicing assays identify 12 spliceogenic variants of BRCA2 exons 14 and 15" by Eladio Andrés Velasco (3369893)

“…So, the ESE/ESS prediction algorithms require further improvement.<br><br>…”

Image 1_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Table_1_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…</p>Results<p>Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms. …”

EMG and data glove dataset for dexterous myoelectric control by Agamemnon Krasoulis (6582983)

“…For all participants (i.e. both able-bodied and amputee), the data glove was worn on the left hand (i.e. contralateral to the arm where the EMG sensors were located). …”

Table_1_Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.docx by Hao Yang (328526)

“…Informed by both biological understanding and the outcomes of algorithms, we meticulously identified the ultimate set of liver metastasis-related gene (LRG). …”

Image_1_Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.tif by Hao Yang (328526)

“…Informed by both biological understanding and the outcomes of algorithms, we meticulously identified the ultimate set of liver metastasis-related gene (LRG). …”

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). …”

FCP dataset for forecasting temperature, PV, price, and load by Hanwen Zhang (18259666)

“…</p><p dir="ltr">• To design and develop data-driven algorithms for accurate and reliable charging supplydemand forecasting and cost-optimal scheduling with large-volume and high-resolution data.…”