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protein optimization » process optimization (Expand Search), property optimization (Expand Search), driven optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
basic protein » based protein (Expand Search), capsid protein (Expand Search)
binary basic » binary mask (Expand Search)
binary b » binary _ (Expand Search)
b model » _ model (Expand Search), a model (Expand Search), 2 model (Expand Search)
protein optimization » process optimization (Expand Search), property optimization (Expand Search), driven optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
basic protein » based protein (Expand Search), capsid protein (Expand Search)
binary basic » binary mask (Expand Search)
binary b » binary _ (Expand Search)
b model » _ model (Expand Search), a model (Expand Search), 2 model (Expand Search)
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Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx
Published 2025“…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”
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Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”