Bootstrap methods for assessing the performance of near‐infrared pattern classification techniques

Smith, Brian; Gemperline, Paul J.

doi:10.1002/cem.715

Cited by 15 publications

(8 citation statements)

References 17 publications

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“…A good

r_{pred}^{2}

of 0.729 from CoMFA‐ReF shows that the model has acceptable predictability. Also, a bootstrapping analysis was performed with 100 runs to evaluate the statistical confidence limits of the derived models. We can determine whether at a given significance level a confidence interval covers zero.…”

Section: Resultsmentioning

confidence: 99%

Docking and pharmacophore‐based alignment comparative molecular field analysis three‐dimensional quantitative structure–activity relationship analysis of dihydrofolate reductase inhibitors by linear and nonlinear calibration methods

Ghasemi

Meftahi

Pirhadi

et al. 2013

Journal of Chemometrics

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Comparative molecular field analysis (CoMFA) studies have been carried out on 2,4‐diamino‐5‐(2_‐arylpropargyl)pyrimidine derivatives such as dihydrofolate reductase (DHFR) anticancer inhibitors. Because of the interoperable results of CoMFA models, they are noteworthy tools in rational drug designs. However, the huge amount of fields generated by this method contributes to its poor predictive ability. In this study, we applied a CoMFA region focusing (CoMFA‐ReF) approach to weight and enhance or attenuate the contribution of lattice points on standard CoMFA interactions. In addition, the genetic algorithm (GA) is used to select interactions that are responsible for the inhibitory activities of these compounds. The selected fields were introduced to multiple linear regression, partial least squares (PLS), support vector machines (SVM), and random forest (RF) regression methods. Among the constructed models and in terms of root mean square predictions (RMSEP), the predictive power of the (CoMFA‐ReF)‐PLS (RMSEP = 0.252) was better than that of the others. The performances of the GA‐RF regression model (RMSEP = 0.383) and GA‐SVM (RMSEP = 0.387) were comparable. The pharmacophore‐based alignment has been used as an intelligent alignment algorithm in the construction of a CoMFA standard model to improve the accuracies according to the (DHFR) protein environment. Docking studies clarified the role of these compounds in the inhibitory and anticancer activities of DHFR. Copyright © 2013 John Wiley & Sons, Ltd.

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“…A good

r_{pred}^{2}

Section: Resultsmentioning

confidence: 99%

Docking and pharmacophore‐based alignment comparative molecular field analysis three‐dimensional quantitative structure–activity relationship analysis of dihydrofolate reductase inhibitors by linear and nonlinear calibration methods

Ghasemi

Meftahi

Pirhadi

et al. 2013

Journal of Chemometrics

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“…Further studies on the finding of the number of retained PCs have been discussed in the literature (Daudin, Duby, and Trecourt, 1988;Besse, 1992;Peres-Neto, Jackson, and Somers, 2005). Smith and Gemperline (2002) compared two parametric bootstrap methods for analysing small data sets in order to improve the estimation of misclassification rates of microcrystalline cellulose.…”

Section: Uncertainty Estimation With a Bootstrap Resamplingmentioning

confidence: 99%

A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis

Batbooti

Ransing

Ransing³

2017

Computers & Industrial Engineering

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A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard's risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently, Ransing et al. (2016) proposed a quality correlation algorithm (QCA) for risk based tolerance synthesis. The quality correlation algorithm is based on the principal component analysis (PCA) and a co-linearity index concept (Ransing et al. 2013). The uncertainty in QCA results on mixed data sets is quantified and analysed in this paper.The uncertainty is quantified using a bootstrap sampling method with bias-corrected and accelerated confidence intervals. The co-linearity indices use the length and cosine angles of loading vectors in a p-dimensional space. The uncertainty for all p-loading vectors is shown in a single co-linearity index plot and is used to quantify the uncertainty in predicting optimal tolerance limits. The effects of resampling distributions are analysed. The QCA tolerance limits are revised after estimating the uncertainty in limits via bootstrap sampling. The proposed approach has been demonstrated by analysing in-process data from a previously published case study. and in-process quality improvement. Highlights An approach to embed ISO 9001:2015's risk based thinking for in-process quality improvement is proposed. The uncertainty in the quality correlation algorithm has quantified using an enhanced bootstrap method. The algorithm determines robust optimal and avoid ranges within the process variation including process interactions.

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“…Depending on the classification algorithm used for identification of rules for assigning the subregion labels, the uncertainty in class membership, which is interpreted as one minus the probability of the test sample belonging to a class represented by one of the subregions, can be assessed in different ways: by resampling, by bootstrapping, or by computing the posterior probability of class membership . With the hierarchical structure of the model, a test sample is passed by means of a sequence of classifications along a path from the root node to a terminal node (the terminal subregion).…”

Section: Construction Of the Tree‐structured Hierarchical Modelmentioning

confidence: 99%

Use of a tree‐structured hierarchical model for estimation of location and uncertainty in multivariate spatial data

Chen

Brown

2014

Journal of Chemometrics

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Analysis and modeling of spatial data are of considerable interest in many applications. However, the prediction of geographical features from a set of chemical measurements on a set of geographically distinct samples has never been explored. We report a new, tree‐structured hierarchical model for the estimation of geographical location of spatially distributed samples from their chemical measurements. The tree‐structured hierarchical modeling used in this study involves a set of geographic regions stored in a hierarchical tree structure, with each nonterminal node representing a classifier and each terminal node representing a regression model. Once the tree‐structured model is constructed, given a sample with only chemical measurements available, the predicted regional location of the sample is gradually restricted as it is passed through a series of classification steps. The geographic location of the sample can be predicted using a regression model within the terminal subregion. We show that the tree‐structured modeling approach provides reasonable estimates of geographical region and geographic location for surface water samples taken across the entire USA. Further, the location uncertainty, an estimate of a probability that a test sample could be located within a pre‐estimated, joint prediction interval that is much smaller than the terminal subregion, can also be assessed. Copyright © 2014 John Wiley & Sons, Ltd.

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Bootstrap methods for assessing the performance of near‐infrared pattern classification techniques

Cited by 15 publications

References 17 publications

Docking and pharmacophore‐based alignment comparative molecular field analysis three‐dimensional quantitative structure–activity relationship analysis of dihydrofolate reductase inhibitors by linear and nonlinear calibration methods

Docking and pharmacophore‐based alignment comparative molecular field analysis three‐dimensional quantitative structure–activity relationship analysis of dihydrofolate reductase inhibitors by linear and nonlinear calibration methods

A bootstrap method for uncertainty estimation in quality correlation algorithm for risk based tolerance synthesis

Use of a tree‐structured hierarchical model for estimation of location and uncertainty in multivariate spatial data

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