Search alternatives:
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
well optimization » wolf optimization (Expand Search), whale optimization (Expand Search), field optimization (Expand Search)
library based » laboratory based (Expand Search)
binary mixed » linear mixed (Expand Search)
based well » based cell (Expand Search), based web (Expand Search), based all (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
well optimization » wolf optimization (Expand Search), whale optimization (Expand Search), field optimization (Expand Search)
library based » laboratory based (Expand Search)
binary mixed » linear mixed (Expand Search)
based well » based cell (Expand Search), based web (Expand Search), based all (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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Seed mix selection model
Published 2022“…</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). …”
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Portable Library for Homomorphic Encrypted Machine Learning on FPGA Accelerated Cloud Cyberinfrastructure
Published 2025“…With the fine-grained programmable architecture of FPGAs, FPGAs are well-suited for accelerating HE ML inference. This project will leverage our novel algorithmic, architectural, and memory optimizations on FPGAs to develop a portable library to enable secure and trustworthy ML inference. …”
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Portable Library for Homomorphic Encrypted Machine Learning on FPGA Accelerated Cloud Cyberinfrastructure
Published 2024“…With the fine-grained programmable architecture of FPGAs, FPGAs are well-suited for accelerating HE ML inference. This project will leverage our novel algorithmic, architectural, and memory optimizations on FPGAs to develop a portable library to enable secure and trustworthy ML inference. …”
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Diversity and specificity of lipid patterns in basal soil food web resources
Published 2019“…In marine environments, multivariate optimization models (Quantitative Fatty Acid Signature Analysis) and Bayesian approaches (source-tracking algorithm) were established to predict the proportion of predator diets using lipids as tracers. …”
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<b>Portable Library for Homomorphic Encrypted Machine Learning on FPGA Accelerated Cloud Cyberinfrastructure</b>
Published 2023“…With the fine-grained programmable architecture of FPGAs, FPGAs are well-suited for accelerating HE ML inference. This project will leverage our novel algorithmic, architectural, and memory optimizations on FPGAs to develop a portable library to enable secure and trustworthy ML inference. …”
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iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules
Published 2022“…Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). …”
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iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules
Published 2022“…Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). …”
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iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules
Published 2022“…Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). …”
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SHAP bar plot.
Published 2025“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”
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Sample screening flowchart.
Published 2025“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”
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Descriptive statistics for variables.
Published 2025“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”
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SHAP summary plot.
Published 2025“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”
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ROC curves for the test set of four models.
Published 2025“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”
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Display of the web prediction interface.
Published 2025“…The optimal model was further assessed for predictor importance utilizing the SHAP method and deployed on a web platform using the Streamlit library.…”
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