DataSheet1_Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model.PDF by Qingliu He (12528332)

“…The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration.…”

Presentation1_Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model.pdf by Qingliu He (12528332)

“…The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration.…”

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

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

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)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

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

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Supporting data for Histone crotonylation is a novel epigenetic regulation and a therapeutic vulnerability for liver cancer treatment by Qidong Li (9069932)

“…Using the ChromHMM machine learning algorithm, we annotated chromatin states based on distinct combinations of these markers. …”

CIAHS-Data.xls by Yingchang Li (22195585)

“…As of this release, a total of 188 CIAHS sites (2013<a href="" target="_blank">–</a>2023) have been identified. Based on their primary functions, we categorized the heritages systems into four types: Planting systems (76 sites, 40.43%), focused on crop cultivation, farmland landscapes, and mixed farming; forestry and fruit systems (92 sites, 48.94%), centered on forestry and fruit planting; animal husbandry systems (15 sites, 7.98%), emphasizing livestock breeding; fisheries systems (5 sites, 2.66%), dedicated to aquaculture. …”

Table 1_Trajectories of health conditions predict cardiovascular disease risk among middle-aged and older adults: a national cohort study.docx by Wenlong Li (571749)

“…Ten machine learning (ML) algorithms were applied to evaluate the predictive capacity of different variable groups for CVD. …”

Table1_Comprehensive pan-cancer analysis and experiments revealed R3HDM1 as a novel predictive biomarker for prognosis and immune therapy response.DOCX by Jiawei Liu (553742)

“…Validation of transcriptome immune infiltration was based on 140 single-cell datasets from the TISCH database. …”

Data Sheet 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.pdf by Lei Pu (608561)

“…A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). …”

Table 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.xlsx by Lei Pu (608561)

“…A total of 101 combinations of 10 machine learning algorithms were used to construct an APIG-based prognostic signature (APIGPS). …”

Supplementary file 1_Almond yield prediction at orchard scale using satellite-derived biophysical traits and crop evapotranspiration combined with machine learning.pdf by Manuel Quintanilla-Albornoz (22653386)

“…In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. …”

Supplementary file 1_Identification of glycolysis-related clusters and immune cell infiltration in hepatic fibrosis progression using machine learning models and experimental valid... by Guanglin Xiao (18113302)

“…Integrated weighted gene co-expression network analysis (WGCNA) with six machine learning algorithms to identify core GRGs genes associated with HF progression, and systematically characterized their biological functions and immunoregulatory roles through immune infiltration assessment, functional enrichment, consensus clustering, and single-cell differential state analysis. …”

Data Sheet 1_Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy.pdf by Xiaote Zhang (21570542)

“…</p>Conclusion<p>An RF-based diagnostic model effectively identifies OME in AH children by integrating anatomical, functional, and inflammatory parameters, providing a clinically applicable tool for enhanced diagnostic accuracy and evidence-based management decisions.…”

Supplementary materials for PhD thesis - "Ontology mapping with intelligent agents on the Semantic Web : the theory and practice of agent belief and consenus building" by Miklos Nagy (22007560)

“…These algorithms form the core part of the DSSirn ontology mapping system that can use different ontology representations based on Semantic Web standards, such as OWL. …”

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). …”

DataSheet1_Multi-omic molecular characterization and diagnostic biomarkers for occult hepatitis B infection and HBsAg-positive hepatitis B infection.docx by Xinyi Jiang (1976353)

“…Prognostic biomarkers were identified using machine learning algorithms, and their validity was confirmed in a larger cohort using enzyme linked immunosorbent assay (ELISA).…”

Data Sheet 2_Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in... by Yifan Xu (87249)

“…</p>Methods<p>We employed functional enrichment algorithms, including AUCell and UCell, to assess lactylation activity in GBM cancer cells. …”

Data Sheet 9_Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in... by Yifan Xu (87249)

“…</p>Methods<p>We employed functional enrichment algorithms, including AUCell and UCell, to assess lactylation activity in GBM cancer cells. …”

Data Sheet 2_Integrated analysis of single-cell RNA-seq and spatial transcriptomics to identify the lactylation-related protein TUBB2A as a potential biomarker for glioblastoma in... by Yifan Xu (87249)

“…</p>Methods<p>We employed functional enrichment algorithms, including AUCell and UCell, to assess lactylation activity in GBM cancer cells. …”