Identification and validation of parthanatos-related genes in end-stage renal disease حسب Xuan Dai (5153786)

"…Two machine learning algorithms identified candidate genes, refined through ROC analysis. …"

Data Sheet 1_Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning.docx حسب Xiaoqing Liu (196900)

"…Key predictors for prognosis post-thrombolysis included the National Institutes of Health Stroke Scale (NIHSS) and blood platelet count. The findings underscore the effectiveness of machine learning algorithms, particularly XGB, in predicting functional outcomes in diabetic AIS patients, providing clinicians with a valuable tool for treatment planning and improving patient outcome predictions based on receiver operating characteristic (ROC) analysis and accuracy assessments.…"

Data Sheet 1_Bioinformatics-based analysis and experimental validation of PANoptosis-related biomarkers and immune infiltration in diabetic nephropathy.zip حسب Su Zhang (411153)

"…Then differentially expressed genes (DEGs) were identified and DEGs were analyzed for functional enrichment. In addition, we obtained key gene modules by WGCNA. …"

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

"…</p><p dir="ltr">1.NanoNeuroBot is an AI-guided, ingestible nanobot pill engineered to cross the blood-brain barrier and deliver site-specific neuronal regeneration. …"

Data Sheet 1_Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets.zip حسب Xinpeng Tian (646275)

"…</p>Methods<p>Differential expression analysis of placental and peripheral blood transcriptomes was performed to identify PE-associated secretory protein genes. …"

Table 3_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx حسب Linyuan Wang (359295)

"…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …"

Table 4_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.docx حسب Linyuan Wang (359295)

"…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …"

Table 5_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.docx حسب Linyuan Wang (359295)

"…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …"

Table 1_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx حسب Linyuan Wang (359295)

"…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …"

Table 2_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx حسب Linyuan Wang (359295)

"…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …"

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). …"

Image1_Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis.pdf حسب Lei Zhong (192135)

"…</p>Results<p>A total of 26 DEGs, with 14 upregulated and 12 downregulated genes, were common between T2DM and AP. According to functional and DisGeNET enrichment analysis, these DEGs were mainly enriched in immune effector processes, blood vessel development, dyslipidemia, and hyperlipidemia. …"

Table1_Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis.xlsx حسب Lei Zhong (192135)

"…</p>Results<p>A total of 26 DEGs, with 14 upregulated and 12 downregulated genes, were common between T2DM and AP. According to functional and DisGeNET enrichment analysis, these DEGs were mainly enriched in immune effector processes, blood vessel development, dyslipidemia, and hyperlipidemia. …"

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

"…Machine learning algorithms (LASSO and Random Forest) were used to identify key diagnostic genes. …"

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

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

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... حسب Haoyan Sun (22172911)

"…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. …"

Raw LC-MS/MS and RNA-Seq Mitochondria data حسب Stefano Martellucci (16284377)

"…Sciatic nerves from scLRP1+/+ and scLRP1-/- mice were rinsed in PBS to remove the blood and frozen with liquid nitrogen in cryogenic storage tubes (#5016-0001, Thermo Fisher Scientific). …"