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Modeling oncology surrogate markers
Journal of Clinical Oncology, 20059639 Background The use of surrogates in clinical trials relies on evidence that treatment benefit on an unmeasured outcome can be reliably predicted from treatment effect on the surrogate.
K. Johnson +5 more
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Introduction to Surrogate Modeling and Surrogate-Based Optimization
2016Surrogate-based optimization (SBO) is the main focus of this book. We provide a brief introduction to the subject in this chapter. In particular, we recall the SBO concept and the optimization flow, discuss the principles of surrogate modeling and typical approaches to construct surrogate models.
Slawomir Koziel, Leifur Leifsson
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Physics-Based Surrogate Modeling
2020Physics-based models constitute the second major class of surrogates. Although they are not as popular as the data-driven models outlined in Chap. 2, their importance is growing because of the challenges related to construction and handling of approximation surrogates for many real-world problems.
Slawomir Koziel +1 more
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2019
An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:
Ping Jiang, Qi Zhou, Xinyu Shao
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An ensemble of surrogate models (EM) is a surrogate model composed of a series of surrogate models combined through a weighted sum. An EM can take advantage of each individual surrogate model to effectively increase the robustness of the prediction. The mathematical expression for an EM can be given as follows:
Ping Jiang, Qi Zhou, Xinyu Shao
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Multi-fidelity Surrogate Models
2019Simulation models are treated as black boxes that generate input–output correspondences. Nevertheless, designers need to choose simulation models with appropriate fidelities to obtain qualities of interest (QOIs) at affordable cost levels. Generally, high-fidelity (HF) simulation models can provide more reliable and accurate simulation results than low-
Ping Jiang, Qi Zhou, Xinyu Shao
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Surrogate Models for Antimalarials
1984In recent years studies concerned with the mode of action of antimalarial drugs such as chloroquine have shifted from those concerned primarily with the effects on a particular enzyme or enzyme system to studies concerned with the consequences of the ability of chloroquine to act as a lysosomotropic agent.
S.-C. Chou +3 more
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Human surrogate models of neuropathic pain
Pain, 2005Neuropathic pain is defined as pain initiated or caused by a primary lesion or dysfunction in the nervous system (Merskey and Bogduk, 1994). Current efforts to refine this definition focus on the terms ‘dysfunction’ and ‘nervous system’ with the intention to clarify that there has to be an identifiable lesion or disease process affecting the ...
Thomas, Klein +3 more
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The Generalized Penalty-Function/Surrogate Model
Operations Research, 1973This paper combines the monotonic-penalty-function and surrogate models into a general model called the penalty-function/surrogate model. It unifies and generalizes the central theorems of earlier papers, and provides some new theorems that can be specialized to the Lagrangian penalty-function model (GLM) or to linear surrogates.
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Optimization with Surrogate Models
2013In this chapter, we show how artificial curiosity can be used to focus on the most pertinent search points in black-box optimization. We present a novel response surface method, which employs a memory-based model to estimate the interestingness of each candidate point using Gaussian process regression.
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