Search alternatives:
method optimization » lead optimization (Expand Search), path optimization (Expand Search), feature optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based method » based methods (Expand Search)
method optimization » lead optimization (Expand Search), path optimization (Expand Search), feature optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based method » based methods (Expand Search)
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Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics
Published 2020“…As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. …”
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<i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling
Published 2019“…To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. …”
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105
An Example of a WPT-MEC Network.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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106
Related Work Summary.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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Simulation parameters.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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Training losses for N = 10.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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Normalized computation rate for N = 10.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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110
Summary of Notations Used in this paper.
Published 2025“…To enhance the offloading decision-making process, the algorithm incorporates the Newton-Raphson method for fast and efficient optimization of the computation rate under energy constraints. …”
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Raw Data for the Thesis: "<i>Enhancing RNAi-Based Pest Control through Effective Target Gene Selection and Optimal dsRNA Design</i>"
Published 2025“…</p><p><br></p><p dir="ltr">Chapter 4 introduces the dsRIP web platform (<a href="https://dsrip.uni-goettingen.de/" target="_blank">https://dsrip.uni-goettingen.de/</a>) for designing sequence-optimized dsRNA for RNAi-based pest control. In the experimental part, small interfering RNA (siRNA) features that were associated with RNAi efficacy in human cells were tested in <i>T. castaneum </i>by targeting an essential gene and measuring insecticidal efficacy. …”
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GSE96058 information.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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The performance of classifiers.
Published 2024“…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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115
DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx
Published 2023“…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …”
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DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx
Published 2023“…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …”
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DataSheet_1_Trans-population graph-based coverage optimization of allogeneic cellular therapy.xlsx
Published 2023“…We propose here a solution to this problem, and test whether it would be more expensive to recruit additional donors or to prevent class I or class II HLA expression through gene editing.</p>Study design<p>We developed an optimal coverage problem, combined with a graph-based algorithm to solve the donor selection problem under different, clinically plausible scenarios (having different HLA matching priorities). …”
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Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures
Published 2023Subjects: -
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Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures
Published 2023Subjects: