Optimal experimental design

Smucker, Byran J.; Krzywinski, Martin; Altman, Naomi

doi:10.1038/s41592-018-0083-2

Cited by 97 publications

(59 citation statements)

References 5 publications

(5 reference statements)

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“…The related literature is wide. Several research areas have addressed the same problem (i.e., AL for regression/emulation) under different names, such as optimal experimental design [59][60][61][62], optimal sensor placements [63,64], generation of quasi-random uniform sequences (Latin Hypercube sampling), Sobol sequences [65,66] and determinantal point processes [67,68], non-uniform, adaptive sampling and quantization of a signal [69,70]. Moreover, adaptive quadrature rules [71,72] and approximations of posterior densities have been introduced [73,74].…”

Section: Active Learning For Emulationmentioning

confidence: 99%

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

et al. 2021

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The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

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Section: Active Learning For Emulationmentioning

confidence: 99%

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

et al. 2021

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“…A D-optimal design approach was used to determine the concentration levels of each analyte for the calibration standards. 28 D-optimal designs maximize the determinant of the information matrix X T X in order to statistically determine the best combination of levels, which here are analyte concentrations, to explore a design space. 29 The purpose of using D-optimal design herein is to reduce the number of calibration standards required to build a regression model without sacrificing predictive quality compared to traditional OFAT calibration set construction.…”

Section: Design Of Experimentsmentioning

confidence: 99%

“…27 Experimental design for multivariate modeling aims to provide more information from fewer training samples by collecting only the information that is needed. 28 This paper discusses the use of Design of Experiments (DoE) to successfully extract calibration standards that adequately describe the structured variation of the data. Calibration models requiring less time and effort than one factor at a time (OFAT) approaches will have a tremendous effect on laboratory- and industrial-scale applications.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy

2020

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Chemical processing of highly radioactive materials commonly takes place in heavily shielded hot cells. The remote, real-time monitoring of chemical processing streams via optical spectroscopic techniques in hot cells may be particularly useful. Here, we describe the implementation of Raman spectroscopy and chemometric analysis to monitor the dissolution of aluminum-clad targets containing irradiated aluminum–neptunium oxide cermet pellets in caustic solutions in a hot cell environment. Partial least squares regression analysis was used to generate calibration models to quantify the concentration of dissolved aluminum, nitrate, and hydroxide in solutions within the radiochemical hot cell. This work explored a systematic approach to optimize a matrix of calibration standards using a D-optimal experimental design. The Design of Experiments-based regression model, in comparison to more traditional analytical approaches, was found to be the more practical method for building calibration models, with fewer samples, to obtain informative analytical data from Raman spectra.

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“…Additionally, machine learning might assist in WCM parameter identification, for example applying Bayesian parameter estimation (Vyshemirsky and Girolami, 2008), regression models and reinforcement learning techniques (Alber et al, 2019). Optimal experimental design techniques might also offer a valuable methodology to select the best experimental datasets for both model identification and validation (Smucker et al, 2018).…”

Section: (Re)thinking System Approaches: a Collaborative Effortmentioning

confidence: 99%

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Marucci

Barberis

Karr

et al. 2020

Front. Bioeng. Biotechnol.

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Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

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Optimal experimental design

Cited by 97 publications

References 5 publications

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

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