Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn’s disease حسب Zhuoyan Chen (12193358)

"…<i>Z</i> score standardization and independent sample <i>t</i> test were applied to identify optimal predictive features, which were then utilized in seven ML algorithms for training and validation. …"

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب Yuhong Huang (115702)

"…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …"

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. حسب Linus Woitke (22783534)

"…Protruding paths are then eliminated using graph-based optimization algorithms, as demonstrated in f). …"

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

"…Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO₂ and IrO₃. …"

Sample image for illustration. حسب Indhumathi S. (19173013)

"…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Comparison analysis of computation time. حسب Indhumathi S. (19173013)

"…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Process flow diagram of CBFD. حسب Indhumathi S. (19173013)

"…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Precision recall curve. حسب Indhumathi S. (19173013)

"…The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. …"

Table_1_iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.docx حسب 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 حسب Massaine Bandeira e Sousa (7866242)

"…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2  = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …"

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

"…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2  = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …"

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx حسب Jun Zhang (48506)

"…The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. …"

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

"…</p> <p>  </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …"

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

"…</p><p dir="ltr">These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. …"