A Combination of MALDI-TOF MS Proteomics and Species-Unique Biomarkers’ Discovery for Rapid Screening of Brucellosis حسب Hamideh Hamidi (13266900)

"…Web-accessible bioinformatics algorithms, with a robust data analysis workflow, followed by ribosomal and structural protein mapping, significantly enhanced the reliable assignment of key proteins and accurate identification of <i>Brucella</i> species. …"

Table_1_Prediction of pCR based on clinical-radiomic model in patients with locally advanced ESCC treated with neoadjuvant immunotherapy plus chemoradiotherapy.docx حسب Xiaohan Wang (691917)

Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg حسب Zhenze Zhang (22011422)

"…</p>Methods<p>Data from 551 IBD patients in the MIMIC-IV database (2008–2019) were analyzed, with external validation using the eICU dataset. Nine ML algorithms (XGBoost, logistic regression, LightGBM, random forest, decision tree, elastic net, MLP, KNN, RSVM) were trained to predict 1-year mortality. …"

Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx حسب Zhenze Zhang (22011422)

"…</p>Methods<p>Data from 551 IBD patients in the MIMIC-IV database (2008–2019) were analyzed, with external validation using the eICU dataset. Nine ML algorithms (XGBoost, logistic regression, LightGBM, random forest, decision tree, elastic net, MLP, KNN, RSVM) were trained to predict 1-year mortality. …"

Table 1_Risk prediction for gastrointestinal bleeding in pediatric Henoch-Schönlein purpura using an interpretable transformer model.doc حسب Gahao Chen (21688843)

"…GI complications were stratified into three severity tiers: 1) no complications, 2) abdominal pain without bleeding), and 3) documented rectal bleeding or hemorrhage, based on standardized diagnostic criteria. Five machine learning algorithms (Random Forest, XGBoost, LightGBM, CatBoost, and TabPFN-V2) were optimized through nested cross-validation. …"

Data Sheet 1_Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy.pdf حسب Xiaote Zhang (21570542)

"…</p>Methods<p>A retrospective analysis was conducted on 847 pediatric patients with AH. Five ML algorithms were developed to identify OME using demographic, clinical, laboratory, and acoustic immittance parameters. …"

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx حسب 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. …"

Table_1_EZcalcium: Open-Source Toolbox for Analysis of Calcium Imaging Data.DOCX حسب Daniel A. Cantu (5543519)

"…However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. …"