Data Sheet 1_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.pdf by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Table 4_Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.xl... by Xiaoyin Wu (15279940)

“…Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. …”

Data Sheet 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung ad... by Lian-jie Ruan (22327876)

“…</p>Materials and methods<p>RNA-seq data from TCGA and GEO were analyzed using Cox regression and 10 machine learning algorithms to identify prognostic genes and stratify patients. …”

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

“…Functional and mechanistic studies were subsequently conducted through in vitro cellular experiments.…”

Image 1_Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.jp... by Xiaoyin Wu (15279940)

“…Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. …”

Table 1_Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.xl... by Xiaoyin Wu (15279940)

“…Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. …”

Image 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.jpeg by Duo Wang (1787634)

“…Moreover, the GPR-TME classifier as the prognosis model was constructed and further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses.…”

Table 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.xlsx by Duo Wang (1787634)

“…Moreover, the GPR-TME classifier as the prognosis model was constructed and further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses.…”

Table 1_Development of an alkaliptosis-related lncRNA risk model and immunotherapy target analysis in lung adenocarcinoma.docx by Xiang Xiong (1810234)

“…Immune cell infiltration and Tumor Mutational Burden (TMB) analyses were carried out using the CIBERSORT and maftools algorithms. Finally, the “oncoPredict” package was employed to predict immunotherapy sensitivity and to further forecast potential anti-tumor immune drugs. qPCR was used for experimental verification.…”

Table 4_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…Featuring an intuitive visual interface and hardware–software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer’s disease as an example, we showcase its functionalities, operational methods, and analytical capabilities.…”

Table 5_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…Featuring an intuitive visual interface and hardware–software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer’s disease as an example, we showcase its functionalities, operational methods, and analytical capabilities.…”

Table 2_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…Featuring an intuitive visual interface and hardware–software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer’s disease as an example, we showcase its functionalities, operational methods, and analytical capabilities.…”

Supplementary file 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial ce... by Jun Shi (289433)

“…Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Table 3_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…Featuring an intuitive visual interface and hardware–software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer’s disease as an example, we showcase its functionalities, operational methods, and analytical capabilities.…”

Table 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.xlsx by Jun Shi (289433)

“…Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Table 1_SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.xlsx by Jidong Lang (802830)

“…Featuring an intuitive visual interface and hardware–software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer’s disease as an example, we showcase its functionalities, operational methods, and analytical capabilities.…”

Table 1_Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experi... by Yaru Shi (2497831)

“…These core intersecting genes were subsequently refined via four complementary machine learning algorithms, KM survival analysis and LASSO Cox regression to establish a prognostic risk score model with predictive value. …”

Table 2_Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experi... by Yaru Shi (2497831)

“…These core intersecting genes were subsequently refined via four complementary machine learning algorithms, KM survival analysis and LASSO Cox regression to establish a prognostic risk score model with predictive value. …”

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

“…The least absolute shrinkage and selection operator (LASSO) regression selected predictors from clinical/neuroimaging/laboratory variables. Eight ML algorithms (including Logistic Regression, Random Forest, Extreme Gradient Boosting, Multilayer Perceptron, Support Vector Machine, Light Gradient Boosting Machine, Decision Tree, and K-Nearest Neighbors) were trained using 10-fold cross-validation and evaluated on test/external sets via the area under the curve (AUC), accuracy, precision, recall and F1-score. …”

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>…”