Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx by Changli He (22424818)

“…</p>Results<p>Combining different imputation, sampling, and feature selection strategies yielded 27 datasets, which were trained across nine algorithms to generate 243 models. The optimal model—Simp-SMOTE_rf_GBM, constructed using random forest imputation, SMOTE oversampling, and gradient boosting machine—achieved the highest predictive performance (AUC = 0.954). …”

DataSheet_2_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…</p>Methods<p>The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.…”

DataSheet_1_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…</p>Methods<p>The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.…”

DataSheet_1_A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn... by Gaosen Zhang (539619)

Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx by Yuan Liu (88411)

“…In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1) k-mer spectrum and (2) positional nucleotide binary vector, to capture the sequential characteristics of 5hmC sites. …”

DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”

Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction by Raul A. Flores (2910539)

“…We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.…”

Seed mix selection model by Bethanne Bruninga-Socolar (10923639)

“…Classic genetic algorithms consider a population of chromosomes and apply principles of natural selection (selection, mutation, and crossover processes) to generate optimal solutions. …”

Supplementary file 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.xlsx by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Image 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Supplementary file 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.docx by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Image 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…Details on the data sourcing process, prompt engineering strategies for large language model (LLM)-based extraction, and validation protocols are provided in the Supplementary Information section.…”