SEARCH · PubMed Health

Results for “Bootstrap resampling”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources

PubMed2005

JACOP: a simple and robust method for the automated classification of protein sequences with modular architecture.

BACKGROUND: Whole-genome sequencing projects are rapidly producing an enormous number of new sequences. Consequently almost every family of proteins now contains hundreds of members. It has thus become necessary to develop tools, which classify protein sequences automatically and also quickly and reliably. The difficulty of this task is intimately linked to the mechanism by which protein sequences diverge, i.e. by simultaneous residue substitutions, insertions and/or deletions and whole domain reorganisations (duplications/swapping/fusion). RESULTS: Here we present a novel approach, which is based on random sampling of sub-sequences (probes) out of a set of input sequences. The probes are compared to the input sequences, after a normalisation step; the results are used to partition the input sequences into homogeneous groups of proteins. In addition, this method provides information on diagnostic parts of the proteins. The performance of this method is challenged by two data sets. The first one contains the sequences of prokaryotic lyases that could be arranged as a multiple sequence alignment. The second one contains all proteins from Swiss-Prot Release 36 with at least one Src homology 2 (SH2) domain--a classical example for proteins with modular architecture. CONCLUSION: The outcome of our method is robust, highly reproducible as shown using bootstrap and resampling validation procedures. The results are essentially coherent with the biology. This method depends solely on well-established publicly available software and algorithms.

Algorithms↗

PubMed2026

Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.

BACKGROUND: Elderly patients with acute myeloid leukemia (AML) exhibit considerable biological and clinical heterogeneity, hindering precise prognosis. Existing prognostic systems inadequately capture the complexity of elderly AML due to their reliance on data from younger cohorts and omission of key factors like immunophenotypic markers and therapeutic profiles. This study aimed to develop and internally validate a machine learning-based prognostic model specifically tailored to elderly AML patients. METHODS: A total of 156 patients were analyzed using a two-stage modeling strategy. Clinical and genomic variables were modeled first, followed by independent analysis of immunophenotypic features. Feature selection was performed using multilayer perceptron (MLP) and random forest (RF), while multivariate Cox regression was used for final model construction. Internal validation was conducted using 1000 bootstrap iterations to assess model stability and performance. RESULTS: The model demonstrated strong predictive performance, with a concordance index (C-index) of 0.702. Time-dependent area under the curve (AUC) and calibration plots confirmed accurate prediction of 1-, 3-, and 5-year overall survival. Decision curve analysis indicated favorable net benefit across a range of threshold probabilities. Key independent prognostic factors identified included TP53 mutations, high CD13 expression, and IDH2 mutations. CONCLUSION: This model provides a robust and interpretable tool for individualized risk stratification in elderly AML. By integrating genomic, immunophenotypic, and therapeutic variables, it may help optimize treatment decisions and improve outcomes for this vulnerable population. Future efforts should focus on external validation and integration of dynamic biomarkers.

Humans↗

PubMed2005

Determining the significance of scale values from multidimensional scaling profile analysis using a resampling method.

Although multidimensional scaling (MDS) profile analysis is widely used to study individual differences, there is no objective way to evaluate the statistical significance of the estimated scale values. In the present study, a resampling technique (bootstrapping) was used to construct confidence limits for scale values estimated from MDS profile analysis. These bootstrap confidence limits were used, in turn, to evaluate the significance of marker variables of the profiles. The results from analyses of both simulation data and real data suggest that the bootstrap method may be valid and may be used to evaluate hypotheses about the statistical significance of marker variables of MDS profiles.

Career Choice↗

PubMed2005

The efficiency of different search strategies in estimating parsimony jackknife, bootstrap, and Bremer support.

BACKGROUND: For parsimony analyses, the most common way to estimate confidence is by resampling plans (nonparametric bootstrap, jackknife), and Bremer support (Decay indices). The recent literature reveals that parameter settings that are quite commonly employed are not those that are recommended by theoretical considerations and by previous empirical studies. The optimal search strategy to be applied during resampling was previously addressed solely via standard search strategies available in PAUP*. The question of a compromise between search extensiveness and improved support accuracy for Bremer support received even less attention. A set of experiments was conducted on different datasets to find an empirical cut-off point at which increased search extensiveness does not significantly change Bremer support and jackknife or bootstrap proportions any more. RESULTS: For the number of replicates needed for accurate estimates of support in resampling plans, a diagram is provided that helps to address the question whether apparently different support values really differ significantly. It is shown that the use of random addition cycles and parsimony ratchet iterations during bootstrapping does not translate into higher support, nor does any extension of the search extensiveness beyond the rather moderate effort of TBR (tree bisection and reconnection branch swapping) plus saving one tree per replicate. Instead, in case of very large matrices, saving more than one shortest tree per iteration and using a strict consensus tree of these yields decreased support compared to saving only one tree. This can be interpreted as a small risk of overestimating support but should be more than compensated by other factors that counteract an enhanced type I error. With regard to Bremer support, a rule of thumb can be derived stating that not much is gained relative to the surplus computational effort when searches are extended beyond 20 ratchet iterations per constrained node, at least not for datasets that fall within the size range found in the current literature. CONCLUSION: In view of these results, calculating bootstrap or jackknife proportions with narrow confidence intervals even for very large datasets can be achieved with less expense than often thought. In particular, iterated bootstrap methods that aim at reducing statistical bias inherent to these proportions are more feasible when the individual bootstrap searches require less time.

Bayes Theorem↗

PubMed1992

Statistical properties of bootstrap estimation of phylogenetic variability from nucleotide sequences. I. Four taxa with a molecular clock.

The statistical properties of sample estimation and bootstrap estimation of phylogenetic variability from a sample of nucleotide sequences are studied by using model trees of three taxa with an outgroup and by assuming a constant rate of nucleotide substitution. The maximum-parsimony method of tree reconstruction is used. An analytic formula is derived for estimating the sequence length that is required if P, the probability of obtaining the true tree from the sampled sequences, is to be equal to or higher than a given value. Bootstrap estimation is formulated as a two-step sampling procedure: (1) sampling of sequences from the evolutionary process and (2) resampling of the original sequence sample. The probability that a bootstrap resampling of an original sequence sample will support the true tree is found to depend on the model tree, the sequence length, and the probability that a randomly chosen nucleotide site is an informative site. When a trifurcating tree is used as the model tree, the probability that one of the three bifurcating trees will appear in > or = 95% of the bootstrap replicates is < 5%, even if the number of bootstrap replicates is only 50; therefore, the probability of accepting an erroneous tree as the true tree is < 5% if that tree appears in > or = 95% of the bootstrap replicates and if more than 50 bootstrap replications are conducted. However, if a particular bifurcating tree is observed in, say, < 75% of the bootstrap replicates, then it cannot be claimed to be better than the trifurcating tree even if > or = 1,000 bootstrap replications are conducted. When a bifurcating tree is used as the model tree, the bootstrap approach tends to overestimate P when the sequences are very short, but it tends to underestimate that probability when the sequences are long. Moreover, simulation results show that, if a tree is accepted as the true tree only if it has appeared in > or = 95% of the bootstrap replicates, then the probability of failing to accept any bifurcating tree can be as large as 58% even when P = 95%, i.e., even when 95% of the samples from the evolutionary process will support the true tree. Thus, if the rate-constancy assumption holds, bootstrapping is a conservative approach for estimating the reliability of an inferred phylogeny for four taxa.

Phylogeny↗

PubMedDate not supplied

Human mesor-hypertensive chronorisk.

Twelve endocrine variables in blood from a small number of clinically healthy adult women were sampled systematically around the clock and the seasons. Pattern discrimination methods singled out certain hormone values in certain seasons as classifiers for a high vs low risk of developing diseases associated with a high blood pressure. Further evidence in support of such classifiers is obtained on data from adolescent, menstrually cycling young adults and post-menopausal women, here analyzed as pool of series, with the scope of the data from any one age group greatly extended by a resampling procedure, namely, by bootstrapping. This mathematical approach was carried out on data series around the clock and seasons on several hormones as well as systolic and diastolic blood pressure. Classifier roles were strongly supported for plasma aldosterone and thyroid stimulating hormone, originally by an analysis of variance and, in the case of aldosterone, by circannual cosinor analysis and by numerical resampling. Circannual bootstrapping, a procedure recommended for broad routine use as a safeguard for hypothesis testing, was also done for plasma cortisol, dehydro-epi-androsterone sulfate and prolactin, variables for which (parametric) analyses of variance and cosinors did not reveal any difference between groups at high and low cardiovascular risk. In these instances, bootstrapping results are tentative and await further analyses. Results show the ability of circannual bootstrapping to detect outliers. Identification of classifiers provides cost-effective endocrine checks complementing the targeted automatic monitoring of blood pressure. Circannual indices for risk evaluation are, however, costly in several ways since it takes at least a year and quite a few samples to estimate them reliably. Accordingly, we also extended the scope of previous results by the application of an added procedure for circadian bootstrapping. With circadian as well as circannual bootstrapping, we here illustrate a major potential component of a system of chrono-engineering for health maintenance. This system should start with focus on the newborn. The results on adults here analyzed are likely to be more prominent in the neonate, to the extent that they are genetic in origin, yet amenable to modification by the extra-uterine environment.

Adolescent↗

PubMed2002

Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.

The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.

Adolescent↗

PubMed2001

Estimation of error and bias in Bayesian Monte Carlo decision analysis using the bootstrap.

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.

Journal Article↗

PubMed1985

Simultaneous small-sample multivariate Bernoulli confidence intervals.

A technique based on the bootstrap is presented for assessing the simultaneous confidence level of k small-sample confidence intervals for multivariate Bernoulli marginal frequencies. The small-sample intervals used are those of Clopper and Pearson (1934, Biometrika 26, 404-413) and require iterative computation. To estimate the simultaneous confidence level, the multivariate Bernoulli vectors are resampled via the bootstrap and the Clopper-Pearson intervals recomputed on each pseudosample. The bootstrap estimate is then the proportion of times (computed via Monte Carlo) that all the k intervals computed by resampling contain the original sample frequencies. The technique is applied to single-sample HLA data.

Analysis of Variance↗

PubMed2006

Increased reliability for single-case research results: is the bootstrap the answer?

There is need for objective and reliable single-case research (SCR) results in the movement toward evidence-based interventions (EBI), for inclusion in meta-analyses, and for funding accountability in clinical contexts. Yet SCR deals with data that often do not conform to parametric data assumptions and that yield results of low reliability. A resampling technique, the bootstrap, largely bypasses statistical assumptions and usually yields more reliable results. This study answers questions about the extent of need for the bootstrap in SCR and its impact on effect size reliability. The bootstrap was applied in Allison et al. mean shift analyses (Faith, Allison, & Gorman, 1997) to data from 166 published AB graphs. Results showed the bootstrap improved reliability of 88% of the analyses and reduced reliability of only 3%. The reliability improvement was large enough to be practically useful. The bootstrap was paired with a method for cleansing data of autocorrelation, which also proved effective. Pending replication, the findings encourage broad application within SCR of both the bootstrap and autocorrelation cleansing.

Case-Control Studies↗

PubMed1996

A comparison of statistical methods for clustered data analysis with Gaussian error.

We investigate by simulation the properties of four different estimation procedures under a linear model for correlated data with Gaussian error: maximum likelihood based on the normal mixed linear model; generalized estimating equations; a four-stage method, and a bootstrap method that resamples clusters rather than individuals. We pay special attention to the group randomized trials where the number of independent clusters is small, cluster sizes are big, and the correlation within the cluster is weak. We show that for balanced and near balanced data when the number of independent clusters is small (< or = 10), the bootstrap is superior if analysts do not want to impose strong distribution and covariance structure assumptions. Otherwise, ML and four-stage methods are slightly better. All four methods perform well when the number of independent clusters reaches 50.

Bias↗

PubMed1990

The defibrillation success rate versus energy relationship: Part II--Estimation with the "bootstrap".

Seventy or so defibrillation trials were typically attempted to determine the relationship between defibrillation success rate and energy (DSRE). Clinically, it may be desirable to estimate the DSRE relationship with fewer trials. We used the statistical resampling technique called the "bootstrap" to determine the number of defibrillation trials necessary for an accurate estimation of the DSRE relationship. The bootstrap technique assumes that the observed database is the maximum likelihood sample of the estimated population. The observed database is repeatedly resampled to produce a large bootstrap data-base and the bootstrap best estimate of a statistic is determined. DSRE data were obtained from ten dogs (20.5 +/- 1.5 kg). We bootstrapped our experimental DSRE data by two methods: (1) randomly choosing with replacement a specified number of defibrillation trials per energy; and (2) randomly choosing with replacement a specified number of defibrillation trials per bootstrap replication. For both bootstrap techniques, 100 replications were made. We performed a linear regression analysis on the bootstrap success rates and the observed success rates determined from 71.0 +/- 6.8 defibrillation attempts from each of the ten dogs. We concluded that 28 defibrillation trials are necessary to estimate the observed DSRE relationship with a correlation coefficient of 0.95.

Animals↗

PubMed2002

Brain expansion in the chick embryo initiated by experimentally produced occlusion of the spinal neurocoel.

The brain expands in the early chick embryo from pressure generated by accumulation of cerebrospinal fluid (CSF) in a closed neural tube. The sealing of the neural tube occurs as the result of occlusion of the spinal neurocoel rostral to and before closure of the posterior neuropore. We have previously demonstrated the dependence of normal brain expansion upon intraluminal pressure. We had yet to demonstrate, however, that brain expansion actually depends upon natural occlusion of the spinal neurocoel. To demonstrate such dependence, we experimentally occluded the spinal neurocoels of embryos 5 hr younger than stage 11 embryos (in which occlusion of the neurocoel occurs naturally). The stage 10 chick embryos were cultured ex ovo and critically staged, and their spinal neurocoels were occluded using microcautery. All embryos were photographed immediately and at 5, 12, and 24 hr after cautery. Serial sections were made of selected embryos, in which the areas of both the brain and the head were measured. Wilcoxon-Mann-Whitney rank-sum nonparametric tests, Hodges-Lehmann estimators, bootstrapping techniques, and resampling randomization tests were used to determine whether the increases in the brain and head areas for the experimental embryos were significantly different from those of the control embryos during three distinct intervals of expansion: 0-5, 5-12, and 0-12 hr. From 0 to 5 hr, the brains of the precociously occluded embryos expanded significantly more than the brains of the non-occluded controls. From 5 to 12 hr, the brains of the embryos with naturally occluded neurocoels grew significantly larger than the brains of the embryos with precociously occluded neurocoels. At 12 hr, there appeared to be no difference in brain size for these two groups. We conclude that the data support the hypothesis that brain expansion is directly dependent upon occlusion of the spinal neurocoel.

Animals↗

PubMed2003

Applying non-parametric statistical methods to the classical measurements of inclusion complex binding constants.

A study on using non-parametric statistical methods was carried out to calculate the binding constant of an inclusion complex and to estimate its associated uncertainty. First, a correct evaluation of the stoichiometry was carried out in order to ensure an accurate determination of the binding constant. For this purpose, the modified Benesi-Hildelbrand method had been previously applied. Then, four statistical methods (three non-parametric methods: two bootstrap approaches, the jackknife method and a parametric one: Fieller's theorem) were employed in order to compute the binding constant. The results obtained from applying these methods and the combination of the methods: jackknife after bootstrap and bootstrap after jackknife were compared. The best results in terms of accuracy were obtained from the application of a bootstrap method: the resampling residuals approach. These procedures were applied to the inclusion complex 2-hydroxil-propyl-beta-cyclodextrin-2,4-dichloro-phenoxyacetic, which shows photochemically-induced fluorescence.

Binding Sites↗

PubMed2002

p values for pathogens: statistical inference from infectious-disease data.

Certain features of infectious-disease data-including the aggregated nature of the data, confounding variables, correlated variables, and non-linear relations-complicate the use of standard statistical procedures. Using data on a helminth infection, we review the use of three parametric tests (analysis of variance, linear regression, and logistic regression), address the complications arising from violation of the assumptions for these tests, and suggest methods of correction. We also compare the relative merits of parametric methods with equivalent non-parametric approaches, and illustrate the differences produced with results from a Kruskal-Wallis test and a t test. The value of using a resampling method-bootstrapping-is also shown. Finally, we discuss problems arising from use of a study design that requires data on the same attribute to be collected from the same individual over a period of time, and present three methods for overcoming this complication, showing that, in the example used, the mixed effect model and generalised estimating equation give similar results.

Analysis of Variance↗

PubMedDate not supplied

Prediction of properties from simulations: a re-examination with modern statistical methods.

We discuss models fit to data collected by Duffy and Jorgensen to predict solvation free energies and partition equilibria of drugs, organic molecules, aromatic heterocycles, and other molecules. These data were originally examined using linear regression, but here more recently developed statistical models are applied. The data set is complicated due to the presence of discrepant observations and also curvature in the response. In some cases it is possible to discard a small number of the observations to get good fit to the data, but, in others, discarding an increasing proportion of the observations does not improve the fit. Our general preference is to use robust parameter estimation which downweights to reduce the influence of discrepant observations on the fitted models. Models are selected for four responses using linear or more complicated representations of the explanatory variables, such as cubic polynomials, B-splines, or smoothers via generalized additive models (GAMs). Variables are chosen using the traditional approach of formal tests to assess contribution to the fit of a model, and resampling methods including bootstrap are also considered to assess the prediction error for given models. Results of our analysis indicate that GAMs are an improvement on linear models for describing the data and making predictions. In general robust regression models and GAMs have the smallest conditional expected loss of prediction over the four responses. In addition, robust regression models offer the advantage of identifying molecules that perform poorly in the fit. In general, models were identified that yielded an improvement of approximately 50% in the conditional expected loss of prediction compared with the original parametrization of Duffy and Jorgensen. It was also found that the use of cross-validation to compare models was unreliable, and bootstrapping is preferred.

Journal Article↗

PubMed2001

Penalized discriminant analysis of [15O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters.

We propose a flexible, comprehensive approach for analysis of [15O]-water positron emission tomography (PET) brain images using a penalized version of linear discriminant analysis (PDA). We applied it to scans from 20 subjects (eight scans/subject) performing a finger movement task and analyzed: 1) two classes to obtain a covariance-normalized baseline-activation image, and 2) eight classes for the mean within subject temporal structure which contained baseline-activation and time-dependent changes in a two-dimensional canonical subspace. We imposed spatial smoothness on the resulting image(s) by expanding it in five tensor-product B-spline (TPS) bases of varying smoothness, and further regularized with a ridge-type penalty on the noise covariance matrix. The discrimination approach of PDA provides a probabilistic framework within which prediction error (PE) estimates are derived. We used these to optimize over TPS bases and a ridge hyperparameter (expressed as equivalent degrees of freedom, EDF). We obtained unbiased, low variance PE estimates using modern resampling tools (.632+ Bootstrap and cross validation), and compared PDA of 1) TPS-projected, mean-normalized and unnormalized scans and 2) mean-normalized scans with and without additional presmoothing. By examining the tradeoffs between PE and EDF, as a function of basis selection and image smoothing we demonstrate the utility of PDA, the PE framework, and the relationship between singular value decomposition and smooth TPS bases in the analysis of functional neuroimages.

Brain↗

PubMed2005

Quantile regression models with multivariate failure time data.

As an alternative to the mean regression model, the quantile regression model has been studied extensively with independent failure time data. However, due to natural or artificial clustering, it is common to encounter multivariate failure time data in biomedical research where the intracluster correlation needs to be accounted for appropriately. For right-censored correlated survival data, we investigate the quantile regression model and adapt an estimating equation approach for parameter estimation under the working independence assumption, as well as a weighted version for enhancing the efficiency. We show that the parameter estimates are consistent and asymptotically follow normal distributions. The variance estimation using asymptotic approximation involves nonparametric functional density estimation. We employ the bootstrap and perturbation resampling methods for the estimation of the variance-covariance matrix. We examine the proposed method for finite sample sizes through simulation studies, and illustrate it with data from a clinical trial on otitis media.

Biometry↗