Search alternatives:
structure optimization » structural optimization (Expand Search), structure determination (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
3d structure » _ structure (Expand Search)
binary naive » binary pairs (Expand Search)
naive model » canine model (Expand Search)
binary 3d » binary _ (Expand Search), binary b (Expand Search)
structure optimization » structural optimization (Expand Search), structure determination (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
3d structure » _ structure (Expand Search)
binary naive » binary pairs (Expand Search)
naive model » canine model (Expand Search)
binary 3d » binary _ (Expand Search), binary b (Expand Search)
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Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data
Published 2022“…An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. Thirty models established by combining five molecular fingerprints (i.e., Morgan, MACCS, RDKit, Pattern, and Layered) and six algorithms [i.e., gradient boosting tree, random forest (RF), multi-layered perceptron, <i>k</i>-nearest neighbor, logistic regression, and naive Bayes] were trained using the selected assay data set. …”
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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...
Published 2022“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”
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Supplementary Material 8
Published 2025“…</p><p dir="ltr">When applied to AMR prediction, SMOTE enhances the ability of classification models to accurately identify resistant <i>Escherichia coli</i> strains by balancing the dataset, ensuring that machine learning algorithms do not overlook rare resistance patterns. …”
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PathOlOgics_RBCs Python Scripts.zip
Published 2023“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”