DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by 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). …”

30-Meter Resolution Dataset of Abandoned and Reclaimed Croplands in Inner Mongolia, China (2000-2022) by Deji Wuyun (18440981)

“…This method enables precise classification of cultivation status and adopts a binary classification strategy with adaptive optimization, improving the efficiency of sample generation for the Random Forest algorithm. …”

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)

“…The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high (RCal2 ranging from 0.72 to 0.99). …”

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

“…The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high (RCal2 ranging from 0.72 to 0.99). …”

Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

“…(E) The threshold value is calculated based on the histogram: it is the mean of the image subtracted by 4 (optimal value defined by trial and error). …”

Fortran & C++: design fractal-type optical diffractive element by I-Lin Ho (13768960)

“…</p> <p>(4) export geometry/optics raw data and figures for binary DOE devices.</p> <p><br></p> <p>[Wolfram Mathematica code "square_triangle_DOE.nb"]:</p> <p>read the optimized binary DOE document (after Fortran & C++ code) to calculate its diffractive fields for comparison.…”

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

“…</p> <p><br></p> <p>The seed mix model is adapted from the seed mix selection model published by Williams and Lonsdorf (2018), which was based on M’Gonigle <em>et al.…”

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

“…Logistic regression emerged as the optimal machine learning algorithm for both DLR models. …”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01–.027 at the resting state and the voxel-based features. …”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by 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. …”