Data Sheet 1_Inflammation-related biomarkers and berberine therapy in post-stroke depression: evidence from bioinformatics, machine learning, and experimental validation.docx by Wei Liu (20030)

“…Immune cell infiltration was analyzed using single-sample gene set enrichment analysis (ssGSEA). …”

Supplementary file 2_The role of α-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learni... by Qing Lu (28914)

“…Sepsis-related targets were obtained from the GEO dataset GSE26440, and the intersection of these datasets was analyzed to reveal common targets. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (L1-LASSO, RF, and SVM) were applied to identify biomarkers. …”

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... by 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. …”

Supplementary file 1_The role of α-hydroxybutyrate in modulating sepsis progression: identification of key targets and biomarkers through multi-database data mining, machine learni... by Qing Lu (28914)

“…Sepsis-related targets were obtained from the GEO dataset GSE26440, and the intersection of these datasets was analyzed to reveal common targets. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (L1-LASSO, RF, and SVM) were applied to identify biomarkers. …”

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>…”

Table 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.xlsx by Liu Haoming (22524473)

“…Top-ranked t00043332 was functionally validated in A549/PC9 cells.</p>Results<p>Ten mtRNAs distinguished cancer from normal tissues. …”

Data Sheet 2_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…Top-ranked t00043332 was functionally validated in A549/PC9 cells.</p>Results<p>Ten mtRNAs distinguished cancer from normal tissues. …”

Data Sheet 1_Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches.csv by Liu Haoming (22524473)

“…Top-ranked t00043332 was functionally validated in A549/PC9 cells.</p>Results<p>Ten mtRNAs distinguished cancer from normal tissues. …”

Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multipl... by Lili Huang (125493)

“…Immune infiltration analysis revealed that in DKD patients, the expression levels of Memory B Cells, CD8⁺ T cells, M1 macrophages, M2 macrophages, resting dendritic cells, and resting mast cells were increased. …”

Table 2_Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning.docx by Jinyu Zheng (734257)

“…Immune profiling revealed elevated CD4⁺ T cells, macrophages, and dendritic cells in LUAD. …”

Table 1_Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning.docx by Jinyu Zheng (734257)

“…Immune profiling revealed elevated CD4⁺ T cells, macrophages, and dendritic cells in LUAD. …”

Supplementary file 1_Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy.xlsx by Xueming Ma (2119150)

“…</p>Methods<p>Multi-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. A variety of algorithms and analysis techniques, such as Kaplan-Meier analysis, Cox regression analysis, and ConsensusClusterPlus clustering analysis, were used to identify PRGs related to the prognosis of bladder cancer patients, and functional analysis and clustering analysis were conducted in depth. …”

Table 1_Bioinformatics revealed biomarkers for diagnosis in kidney stones.xls by Ziqi He (2507899)

“…Following this, among the 8 DE-FRGs, LASSO and SVM-RFE algorithms chose FZD7, STK11, SUV39H1, and LCN2 as marker genes with suitable diagnostic capabilities. …”

Table 2_Bioinformatics revealed biomarkers for diagnosis in kidney stones.xlsx by Ziqi He (2507899)

“…Following this, among the 8 DE-FRGs, LASSO and SVM-RFE algorithms chose FZD7, STK11, SUV39H1, and LCN2 as marker genes with suitable diagnostic capabilities. …”

Table 1_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.docx by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 3_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 1_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 2_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Supplementary Material for: FCGR3A Drives Innate Immune Activation via M1 Macrophage Polarization in Pediatric IBD by figshare admin karger (2628495)

“…In vitro, FCGR3A was upregulated in LPS-induced epithelial cells, and its knockdown inhibited M1 macrophage polarization. …”

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

“…Below are the relevant issues to consider:</p><p dir="ltr">(1) The <i>morality</i> exclusion when using human embryonic stem cells for the bioink culture.</p><p dir="ltr">(2) The hurdle of patenting software or algorithms for printing will be assessed via the technical contribution and the problem/solution approach.…”