Table 3_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

Table 6_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

Table 2_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

Table 4_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

Table 7_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

Table 1_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

Table 8_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx حسب Filippo Guerri (17017524)

"…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …"

<b>NanoNeuroBot: Beyond Healing, Toward Human Connection</b> حسب ahmed hossam (21420446)

"…It uses a flexible electrode array, EMG signal sensors, and a smart AI app (built on TensorFlow and Flutter) to optimize stimulation patterns. …"

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). …"

Patentability of 3D bioprinting technologies حسب Phoebe Li (4463947)

"…The production of bioprinting typically involves three phases: pre-printing, printing and post-printing stages. …"

Image 2_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.jpeg حسب Seungmi Kim (11071440)

"…Using these predictors, boosting models outperformed other algorithms, with CatBoost achieving the best performance (ROC-AUC = 0.813 ± 0.014; PR-AUC = 0.748 ± 0.019).…"

Image 1_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.jpeg حسب Seungmi Kim (11071440)

"…Using these predictors, boosting models outperformed other algorithms, with CatBoost achieving the best performance (ROC-AUC = 0.813 ± 0.014; PR-AUC = 0.748 ± 0.019).…"

Data Sheet 2_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.pdf حسب Seungmi Kim (11071440)

"…Using these predictors, boosting models outperformed other algorithms, with CatBoost achieving the best performance (ROC-AUC = 0.813 ± 0.014; PR-AUC = 0.748 ± 0.019).…"

Image 3_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.jpeg حسب Seungmi Kim (11071440)

"…Using these predictors, boosting models outperformed other algorithms, with CatBoost achieving the best performance (ROC-AUC = 0.813 ± 0.014; PR-AUC = 0.748 ± 0.019).…"

Table 1_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.xlsx حسب Seungmi Kim (11071440)

"…Using these predictors, boosting models outperformed other algorithms, with CatBoost achieving the best performance (ROC-AUC = 0.813 ± 0.014; PR-AUC = 0.748 ± 0.019).…"

Data Sheet 1_Development of machine learning models with explainable AI for frailty risk prediction and their web-based application in community public health.pdf حسب Seungmi Kim (11071440)

"…Using these predictors, boosting models outperformed other algorithms, with CatBoost achieving the best performance (ROC-AUC = 0.813 ± 0.014; PR-AUC = 0.748 ± 0.019).…"

Table 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung adenoca... حسب Lian-jie Ruan (22327876)

"…PTGES3 expression was evaluated via tissue microarray immunohistochemistry. Functional assays (CCK-8, colony formation, flow cytometry, Western blot) after lentiviral knockdown in lung cancer cells assessed its effects on proliferation, apoptosis, and cell cycle. …"

Data Sheet 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung ad... حسب Lian-jie Ruan (22327876)

"…PTGES3 expression was evaluated via tissue microarray immunohistochemistry. Functional assays (CCK-8, colony formation, flow cytometry, Western blot) after lentiviral knockdown in lung cancer cells assessed its effects on proliferation, apoptosis, and cell cycle. …"

Table 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.xlsx حسب Ke Ma (260231)

"…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …"

Presentation 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.zip حسب Ke Ma (260231)

"…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …"