Table1_Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data.docx by Erli Wu (17785482)

“…By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. …”

Table4_Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data.xlsx by Erli Wu (17785482)

“…By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. …”

Table3_Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data.xlsx by Erli Wu (17785482)

“…By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. …”

Image1_Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data.pdf by Erli Wu (17785482)

“…By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. …”

Image2_Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data.pdf by Erli Wu (17785482)

“…By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. …”

Image 1_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…The correlation between candidate genes and drug-resistant phenotypes was inferred using pancreatic cancer cell lines, mouse models, and clinical patient data. Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Image 3_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…The correlation between candidate genes and drug-resistant phenotypes was inferred using pancreatic cancer cell lines, mouse models, and clinical patient data. Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Image 2_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…The correlation between candidate genes and drug-resistant phenotypes was inferred using pancreatic cancer cell lines, mouse models, and clinical patient data. Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Table 2_Identification of regulatory cell death-related genes during MASH progression using bioinformatics analysis and machine learning strategies.xlsx by Zhiqiang Lin (747094)

“…A total of 101 combinations of 10 machine learning algorithms were employed to screen for characteristic RCD-related differentially expressed genes (DEGs) that reflect the progression of MASH. …”

Table 1_Identification of regulatory cell death-related genes during MASH progression using bioinformatics analysis and machine learning strategies.xlsx by Zhiqiang Lin (747094)

“…A total of 101 combinations of 10 machine learning algorithms were employed to screen for characteristic RCD-related differentially expressed genes (DEGs) that reflect the progression of MASH. …”

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…”