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codon optimization » wolf optimization (Expand Search)
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based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
way model » many model (Expand Search), clay model (Expand Search), a model (Expand Search)
codon optimization » wolf optimization (Expand Search)
model optimization » global optimization (Expand Search), based optimization (Expand Search), wolf optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
way model » many model (Expand Search), clay model (Expand Search), a model (Expand Search)
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Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model
Published 2019“…A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …”
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Thesis-RAMIS-Figs_Slides
Published 2024“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”
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DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx
Published 2021“…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). …”