Comparison of Order Splitting. by Huiyue Xu (12022388)

Supplementary file 1_Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models.pdf by Wentian Li (28857)

Schematic illustration of spike detection and sequence detection. by Tjitse van der Molen (20391106)

Data for "Machine Learning-based Prediction of Passive Gears from Vessel Tracking Data in Small-Scale Multi-gear Fisheries" by Pamela Lattanzi (16454871)

Supplementary file 1_Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients.doc by Xiaodan Yang (746617)

“…Objective<p>To construct a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients based on multiple machine-learning algorithms, providing decision support to identify high-risk patients and ensure rational clinical medication use.…”

Data Sheet 1_Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke.zip by Yi Cao (229829)

“…Among the four machine learning algorithms evaluated [XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes], the LR model demonstrated robust and consistent performance in predicting SAP among older adult patients with hemorrhagic stroke across multiple evaluation metrics. …”

Data Sheet 2_Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke.docx by Yi Cao (229829)

“…Among the four machine learning algorithms evaluated [XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes], the LR model demonstrated robust and consistent performance in predicting SAP among older adult patients with hemorrhagic stroke across multiple evaluation metrics. …”

Code by Baoqiang Chen (21099509)

“…The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.</p><p><br></p><p dir="ltr">The designed mRNA 5′ UTRs were selected by fixing the MRD and selecting the high and low DynaRDS^syn 5′ UTRs. …”

Core data by Baoqiang Chen (21099509)

“…The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.</p><p><br></p><p dir="ltr">The designed mRNA 5′ UTRs were selected by fixing the MRD and selecting the high and low DynaRDS^syn 5′ UTRs. …”