Table 3_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx by Yan Wu (70890)

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”

Table 8_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx by Yan Wu (70890)

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”

Table 6_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx by Yan Wu (70890)

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”

Data Sheet 3_Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke.zip by Fengli Ye (22123540)

“…PPI network analysis highlighted IL1B, IL6, and CCL2 as central nodes. Machine learning identified four hub genes—LEF1, ITGAX, BLVRB, and ATF4. …”

Data Sheet 4_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …”

Data Sheet 1_Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke.zip by Fengli Ye (22123540)

“…PPI network analysis highlighted IL1B, IL6, and CCL2 as central nodes. Machine learning identified four hub genes—LEF1, ITGAX, BLVRB, and ATF4. …”

Data Sheet 2_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv by Ge Jin (347352)

“…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …”

Table 2_Unraveling the role of coagulation-related genes in esophageal squamous cell carcinoma: development of a prognostic model and exploration of potential clinical significance... by Langlang Deng (22381786)

“…By applying machine learning algorithms, we identified coagulation-related genes in ESCC and developed a predictive model with clinical relevance. …”

Data Sheet 5_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …”

Data Sheet 2_Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke.zip by Fengli Ye (22123540)

“…PPI network analysis highlighted IL1B, IL6, and CCL2 as central nodes. Machine learning identified four hub genes—LEF1, ITGAX, BLVRB, and ATF4. …”

Table 7_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx by Yan Wu (70890)

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”

Table 4_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx by Yan Wu (70890)

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”

Table 1_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx by Yan Wu (70890)

“…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …”

Data Sheet 1_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx by Ge Jin (347352)

“…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …”

Data Sheet 3_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv by Ge Jin (347352)

“…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …”

Table 1_Unraveling the role of coagulation-related genes in esophageal squamous cell carcinoma: development of a prognostic model and exploration of potential clinical significance... by Langlang Deng (22381786)

“…By applying machine learning algorithms, we identified coagulation-related genes in ESCC and developed a predictive model with clinical relevance. …”

Identification of potential circadian rhythm-related hub genes and immune infiltration in preeclampsia through bioinformatics analysis by Juan Tang (437969)

“…Molecular subtyping based on their expression revealed two PE subtypes with distinct immune infiltration patterns and biological functions. Regulatory network construction highlighted potential upstream mechanisms.…”

Data Sheet 1_Differential neuropilin isoform expressions highlight plasticity in macrophages in the heterogenous TME through in-silico profiling.docx by Hyun-Jee Han (20858765)

“…Datasets were processed using established bioinformatics pipelines, including clustering algorithms, to determine cellular heterogeneity and quantify NRP isoform expression within distinct macrophage populations. …”

Data Sheet 2_Differential neuropilin isoform expressions highlight plasticity in macrophages in the heterogenous TME through in-silico profiling.docx by Hyun-Jee Han (20858765)

“…Datasets were processed using established bioinformatics pipelines, including clustering algorithms, to determine cellular heterogeneity and quantify NRP isoform expression within distinct macrophage populations. …”

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>…”