Collaborative research: CyberTraining: Implementation: Medium: Training users, developers, and instructors at the chemistry/physics/materials science interface by Francesco Paesani (5128004)

“…Using computational tools as functional components of discipline-specific curricula and adopting informal learning events allow us to overcome common barriers given by feelings of non-belonging and low self-confidence, which are typical of learning programming for non-computer-science students.…”

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>…”

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. …”

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. …”

Desmoke-LAP: Desmoking in Laparoscopic Surgery Dataset by Yirou Pan (22560914)

“…<p dir="ltr"><b>This is the publicly available dataset from robot-assisted laparoscopic hysterectomy surgery providing a benchmark for designing and validating smoke removal algorithms. </b> </p><h3><b>Overview</b></h3><p dir="ltr">The dataset contains frames and video clips from 10 robot-assisted laparoscopic hysterectomy procedure videos. …”

<b>dGenhancer v2</b>: A software tool for designing oligonucleotides that can trigger gene-specific Enhancement of Protein Translation. by Adam Master (20316450)

“…<br> An excel-based calculator - dGenhancer can be used to search for putative 5’UTR cis-acting elements, which functional activity could be determined by Gibbs energy-dependent secondary structure formation. …”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Data Sheet 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”