Image 1_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png by Min Liang (363007)

“…</p>Conclusions<p>This study presents a robust machine learning model and a web-based tool that assist healthcare practitioners in personalized clinical decision-making and treatment optimization for ASC patients following primary tumor resection.…”

Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

“…An Affymetrix HTA2.0 array and qRT-PCR were applied to screen new specific lncRNA markers for TB in individual nucleated cells from host peripheral blood. A ML algorithm was established to combine the patients’ EHR information and lncRNA data via logistic regression models and nomogram visualization to differentiate PTB from suspected patients of the selection cohort.…”

Data_Sheet_1_Comparison of Radiomics-Based Machine-Learning Classifiers in Diagnosis of Glioblastoma From Primary Central Nervous System Lymphoma.PDF by Chaoyue Chen (7298480)

Data_Sheet_1_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.docx by Qin-Yu Zhao (10014626)

Data_Sheet_2_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.xlsx by Qin-Yu Zhao (10014626)

Overview of SPAM-XAI model complete architecture. by Mohd Mustaqeem (19106494)

“…However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. …”

S1 Code - by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

The flow chart for the study. by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

ROC curve of six impact indicators. by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

“…Machine learning analysis revealed significant influences of catalyst type, catalyst concentration, and the ratio of aldehyde-to-ketone on reaction efficiency. A kinetic model was established by focusing on two primary reactions: Cy = O oxidation (Reaction I) and PRA auto-oxidation (Reaction II), from which the reliable kinetic parameters were obtained via genetic algorithm-based optimization. …”

Machine Learning-Driven Kinetic Elucidation for Sustainable Solvent-Free Continuous ε‑Caprolactone Production via Propionaldehyde-Mediated Nanocarbon Catalysis by Qing Hu (152962)

“…Machine learning analysis revealed significant influences of catalyst type, catalyst concentration, and the ratio of aldehyde-to-ketone on reaction efficiency. A kinetic model was established by focusing on two primary reactions: Cy = O oxidation (Reaction I) and PRA auto-oxidation (Reaction II), from which the reliable kinetic parameters were obtained via genetic algorithm-based optimization. …”

Big Data Model Building Using Dimension Reduction and Sample Selection by Lih-Yuan Deng (17081779)

“…The proposed subdata can retain most characteristics of the original big data. It is also more robust that one can fit various response model and select the optimal model. …”

Data_Sheet_1_Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients.docx by Yuan-yuan Guo (13789975)

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. …”

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning by Lu Xin (728966)

“…About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. …”

Data Sheet 1_Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay.docx by Anusha Klett (20748443)

Image_1_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIFF by Zirui Meng (10671261)

Image_2_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIFF by Zirui Meng (10671261)

Image_3_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIF by Zirui Meng (10671261)

Results of Comprehensive weighting. by Hao Yang (328526)

“…The model is developed and validated using data from 159 debris flow-prone gullies, integrating deep convolutional, recurrent, and attention-based architectures, with hyperparameters autonomously optimized by IKOA. …”