بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm blood » algorithm based (توسيع البحث), algorithm flow (توسيع البحث), algorithm both (توسيع البحث)
blood function » blood donation (توسيع البحث), based function (توسيع البحث), broad functional (توسيع البحث)
algorithm fc » algorithm etc (توسيع البحث), algorithm pca (توسيع البحث), algorithms mc (توسيع البحث)
fc function » _ function (توسيع البحث), a function (توسيع البحث), 1 function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm blood » algorithm based (توسيع البحث), algorithm flow (توسيع البحث), algorithm both (توسيع البحث)
blood function » blood donation (توسيع البحث), based function (توسيع البحث), broad functional (توسيع البحث)
algorithm fc » algorithm etc (توسيع البحث), algorithm pca (توسيع البحث), algorithms mc (توسيع البحث)
fc function » _ function (توسيع البحث), a function (توسيع البحث), 1 function (توسيع البحث)
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921
Data Sheet 1_Diagnostic lncRNA biomarkers and immune-related ceRNA networks for osteonecrosis of the femoral head in metabolic syndrome identified by plasma RNA sequencing and mach...
منشور في 2025"…The MetS dataset from the Gene Expression Omnibus (GEO) was integrated, and weighted gene co-expression network analysis (WGCNA), functional enrichment, protein-protein interaction (PPI) network analysis, MCODE, CytoHubba-MCC, and random forest (RF) algorithms were employed to identify hub mRNAs and their associated lncRNAs. …"
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922
Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx
منشور في 2025"…Introduction<p>Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). …"
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923
Expression vs genomics for predicting dependencies
منشور في 2024"…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. …"
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924
Image_3_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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925
Image_1_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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926
Image_2_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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927
DataSheet_1_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subt...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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928
Image_2_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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929
DataSheet_1_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subt...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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930
Image_1_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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931
Image_3_Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes...
منشور في 2023"…In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. …"
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932
An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows
منشور في 2025"…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). …"