Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site by Mengyan Zhang (6686543)

“…We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. …”

Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models by Ping Hou (17213)

“…To reduce the manual tuning effort on optimal network architecture, a genetic algorithm is investigated to automatically search and configure the network architecture. …”

Description of the dataset. by Davide Ferrari (163517)

“…We further assess its performance by comparing it to established ML algorithms using both naturally imbalanced real-world data and data that has been balanced through oversampling techniques. …”

DataSheet_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal chola... by Di Wang (329735)

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_1_Analytical validation of the 7-gene biosignature for prediction of recurrence risk and radiation therapy benefit for breast ductal carcinoma in situ.docx by David Dabbs (15663155)

Image_1_Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.tif by Qi Wang (22418)

“…</p>Methods<p>To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. …”