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where functional » whose functional (Expand Search), severe functional (Expand Search), three functional (Expand Search)
algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
python function » protein function (Expand Search)
algorithm cl » algorithm co (Expand Search), algorithm _ (Expand Search), algorithm b (Expand Search)
cl function » l function (Expand Search), cell function (Expand Search), cep function (Expand Search)
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Summary of results of naïve Bayes algorithms.
Published 2024“…Algorithms trained without auditory variables as features were statistically worse (p < .001) in both the primary measure of area under the curve (0.82/0.78) and the secondary measure of accuracy (72.3%/74.5%) for the Gaussian and kernel algorithms respectively.…”
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Machine Learning-Aided Structure Determination for TiCl<sub>4</sub>–Capped MgCl<sub>2</sub> Nanoplate of Heterogeneous Ziegler–Natta Catalyst
Published 2019“…Here, we report nonempirical structure determination of TiCl<sub>4</sub>-capped MgCl<sub>2</sub> nanoplates that is based on the combination of a genetic algorithm and density functional calculations. …”
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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data
Published 2019“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”
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Python code for a rule-based NLP model for mapping circular economy indicators to SDGs
Published 2025“…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…”
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Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics
Published 2021“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”
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Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics
Published 2021“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”
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Performance of the three algorithms.
Published 2024“…<div><p>Disruptive events cause decreased functionality of transportation infrastructures and enormous financial losses. …”
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State Function-Based Correction: A Simple and Efficient Free-Energy Correction Algorithm for Large-Scale Relative Binding Free-Energy Calculations
Published 2025“…We present an efficient and straightforward State Function-based Correction (SFC) algorithm, which leverages the state function property of free energy without requiring cycle identification. …”
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157
Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling
Published 2019“…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”
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Rosenbrock function losses for .
Published 2025“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. The method has been successfully applied to real-world steel alloy optimization, where it achieved superior performance while maintaining all metallurgical composition constraints.…”