ArticleSpring simulation conference (SpringSim)2020
ROBUSTNESS OF MULTIDIMENSIONAL OPTIMIZATION OUTCOMES: A GENERAL APPROACH AND A CASE STUDY.
Article in Spring simulation conference (SpringSim), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent.Journal of chemical information and modeling · 2024Article
- An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor.Molecules (Basel, Switzerland) · 2021Article
- MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor.bioRxiv : the preprint server for biology · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
In multidimensional parameter optimization of complex systems, the preferred solution must also be robust to virtually inevitable perturbations and uncertainties. Having a conceptually simple and computationally facile metric that can help distinguish between candidate optimum solutions in a post processing step is useful. Motivated by free energy function in statistical physics, which evaluates a trade-off between entropy and enthalpy, here we introduce a novel statistical robustness metric that assesses robustness with respect to possible to inevitable uncertainties in the objective function values or optimal parameters. The metric is the expected value
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.