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At least 1,207 records · Page 67Linked to original sources
PubMed2004

Bayesian analysis of two-level nonlinear structural equation models with continuous and polytomous data.

Two-level structural equation models with mixed continuous and polytomous data and nonlinear structural equations at both the between-groups and within-groups levels are important but difficult to deal with. A Bayesian approach is developed for analysing this kind of model. A Markov chain Monte Carlo procedure based on the Gibbs sampler and the Metropolis-Hasting algorithm is proposed for producing joint Bayesian estimates of the thresholds, structural parameters and latent variables at both levels. Standard errors and highest posterior density intervals are also computed. A procedure for computing Bayes factor, based on the key idea of path sampling, is established for model comparison.

Bayes Theorem↗
PubMed2006

Bayesian inference of form and shape.

The ability to visually perceive two-dimensional (2D) form and three-dimensional (3D) shape is one of our most fundamental faculties. This ability relies on considerable prior knowledge about the way edge elements in an image are likely to be connected together into a contour as well as the way these 2D contours relate to 3D shapes. The interaction of prior knowledge with image information is well modeled within a Bayesian framework. We review here the experimental evidence of shape perception seen as a Bayesian inference problem.

Bayes Theorem↗
PubMed1997

Factors affecting susceptibility to intramammary infection and mastitis: an approximate Bayesian analysis.

Susceptibility to IMI and to mastitis in Holstein cows was studied using logistic mixed effects models and an approximate Bayesian analysis. Dichotomous response variables were the presence or absence of IMI, caused by any microorganism. IMI caused by Staphylococcus spp. or Corynebacterium spp., and clinical mastitis caused by any microorganism at specific lactation stages. Data included 619 lactation records from 282 cows. Fixed explanatory variables in the model were period, season and age at calving, lactation number, log-transformed SCC, and a joint effect of age and log SCC. Because random cow effects were assumed to be normally distributed and to have an unknown variance, this parameter was estimated by approximate marginal maximum likelihood. Results from the Bayesian analysis were contrasted with maximum likelihood estimates obtained from a fixed effects logistic model that ignored cow effects. Posterior mode and maximum likelihood estimates of location parameters were similar, although standard errors of the maximum likelihood estimates understated uncertainty. The IMI status during a previous lactation was a poor predictor of IMI status in subsequent lactations, and susceptibility increased as SCC increased. Interlactation (logit scale) repeatability estimates of susceptibility ranged from 0.22 to 0.23. A Taylor series expansion was used to approximate correlations between lactations on a binary scale. These correlations depended on associated fixed effects and ranged between 0.12 and 0.18, which were lower than correlations using the logit scale.

Animals↗
PubMed1996

Bayesian analysis of foraging by pigeons (Columba livia).

In this article, the authors combine models of timing and Bayesian revision of information concerning patch quality to predict foraging behavior. Pigeons earned food by pecking on 2 keys (patches) in an experimental chamber. Food was primed for only 1 of the patches on each trial. There was a constant probability of finding food in a primed patch, but it accumulated only while the animals searched there. The optimal strategy was to choose the better patch first and remain for a fixed duration, thereafter alternating evenly between the patches. Pigeons were nonoptimal in 3 ways: (a) they departed too early, (b) their departure times were variable, and (c) they were biased in their choices after initial departure. The authors review various explanations of these data.

Animals↗
PubMed2002

Evaluation of predictability for vancomycin dosage regimens by the Bayesian method with Japanese population pharmacokinetic parameters.

The predictability of serum vancomycin (VCM) concentrations by means of the Bayesian method was evaluated to establish whether the method can be used to select safe and effective VCM treatment regimens for individual patients. Serum VCM concentrations at the trough and 2 h after the end of infusion (peak) were measured. Pharmacokinetic parameters were calculated for VCM dosage regimens based on a two-compartment model with the Bayesian method, using the Japanese population pharmacokinetic parameters estimated by Yasuhara et al. (1998). The predictive performance for serum VCM concentrations and the dosage regimens were analyzed using two points of serum VCM concentration in 41 patients whose serum creatinine and age were in the ranges of 0.4-4.6 mg/dl and 24-92 years, respectively. Although the predicted values for trough and peak VCM concentrations were slightly lower than measured VCM concentrations, the predictive performance was generally good. There were no differences among the groups classified by serum creatinine or age. An examination of predicted data that differed markedly from the measured serum VCM concentrations indicated that a larger difference in volume of distribution at the steady state (Vdss) calculated from serum VCM concentrations at the beginning and revision of dosage regimens resulted in a poorer correlation of predicted values and measured values. This finding indicates that therapeutic drug monitoring should be conducted frequently, and the dosage regimen revised accordingly, in the case of patients who may have a change of Vdss of VCM, for example, due to a complication such as heart failure or edema.

Adult↗
PubMed2005

Prediction of siRNA knockdown efficiency using artificial neural network models.

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.

Algorithms↗
PubMed2006

Quantitative assessment of seminal vesicle and prostate drug concentrations by use of a noninvasive method.

BACKGROUND: The male genital tract is a complex collection of anatomically and biochemically distinct compartments that contribute to the ejaculate. Understanding the pharmacokinetics in these compartments should inform rational therapeutics involving these glands. METHODS: Nineteen men were administered a single dose of 600 mg chloroquine (base) and 975 mg aspirin before providing a semen sample by masturbation with fractionation into a 5-compartment collection device. Fractions were assayed for fructose (unique seminal vesicle marker), prostate-specific antigen (unique prostate marker), salicylate, and chloroquine. Seminal vesicle and prostate concentrations of salicylate and chloroquine were estimated via a novel analytic method involving a multilevel latent-variable model implemented by use of Bayesian methods. RESULTS: The geometric mean chloroquine semen/blood ratio was 4.02 (95% confidence interval [CI], 2.36-6.86); for salicylate, the primary metabolite of aspirin, the semen/blood ratio was 0.10 (95% CI, 0.08-0.14). The estimated mean prostate/seminal vesicle ratio for salicylate, 0.38 (95% CI by Bayesian methods, 0.12-0.73), was consistent with our hypothesis that salicylate would achieve higher concentrations in the seminal vesicle than in the prostate. Chloroquine, however, did not demonstrate a statistically significant seminal vesicle/prostate difference (4.41; 95% CI by Bayesian methods, 0.14-30.52). CONCLUSIONS: We successfully demonstrated the quantitative, noninvasive estimation of drug concentrations in the prostate gland fluid distinct from the seminal vesicle fluid using our optimized method of split-ejaculate collection and a novel mixed-effects model with Bayesian estimation. Our methods can be applied to gland-specific quantitation of drugs and other substances of interest, thus enabling pharmacokinetic, pharmacodynamic, and pathophysiologic studies to inform rational therapeutics within different glands of the male genital tract.

Adult↗
PubMed2025

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Bayes Theorem↗
PubMed2002

The pharmacokinetics and cardiovascular effects of a single intravenous dose of protamine in normal volunteers.

UNLABELLED: Despite its long use in clinical medicine, protamine concentrations and pharmacokinetics in humans have not been reported. The occasional reoccurrence of anticoagulation after protamine reversal of heparin led us to hypothesize that protamine plasma concentrations decrease rapidly. We developed a method for the measurement of protamine in plasma. Eighteen fit volunteers gave their consent to receive 0.5 mg/kg protamine sulfate administered IV by an infusion pump over 10 min. Heart rate, mean arterial blood pressure, and cardiac output, all measured noninvasively, were recorded and blood samples obtained during and after protamine infusion. Blood plasma was subjected to solid-phase extraction and high-performance liquid chromatography. The administration of protamine was associated with no significant changes in heart rate, mean arterial blood pressure, or cardiac output. Plasma protamine concentrations decreased rapidly, becoming nondetectable within approximately 20 min. Protamine elimination differed significantly between men and women: men had significantly larger areas under the concentration versus time curve. Model-independent pharmacokinetic analysis revealed median (range) values as follows: volume of distribution at steady state, 12.3 (6.9--63.1) L; clearance, 2.2 (1.1--12.1) L/min; and t1/2, 7.4 (5.9--9.3) min. Concentration versus time plots revealed an atypical pattern inconsistent with usual exponential models. The Schwartz-Bayesian criterion identified a one-compartment Michaelis-Menten model and a two-compartment exponential model with irreversible binding as performing better than conventional one- or two-compartmental exponential models; however, performance errors were large with both Michaelis-Menten and exponential models. All models described rapid decreases in protamine blood concentrations. IMPLICATIONS: We developed a method for measurement of protamine in human blood. In volunteers, protamine concentrations decreased rapidly after administration. The rapid disappearance of protamine from the circulation, as defined by a median half-life of 7.4 min, could contribute to cases of "heparin rebound" after initial adequate reversal of heparin.

Adult↗
PubMed2006

Nonparametric estimation of natural selection on a quantitative trait using mark-recapture data.

Assessing natural selection on a phenotypic trait in wild populations is of primary importance for evolutionary ecologists. To cope with the imperfect detection of individuals inherent to monitoring in the wild, we develop a nonparametric method for evaluating the form of natural selection on a quantitative trait using mark-recapture data. Our approach uses penalized splines to achieve flexibility in exploring the form of natural selection by avoiding the need to specify an a priori parametric function. If needed, it can help in suggesting a new parametric model. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data for a wild population of sociable weavers (Philetairus socius) to investigate survival in relation to body mass. In agreement with previous parametric analyses, we found that lighter individuals showed a reduction in survival. However, the survival function was not symmetric, indicating that body mass might not be under stabilizing selection as suggested previously.

Animals↗
PubMed2005

Strand separation in negatively supercoiled DNA.

We consider Benham's model for strand separation in negatively supercoiled circular DNA, and study denaturation as function of the linking difference density kappa<0. We propose a statistical version of this model, based on bayesian segmentation methods of current use in bioinformatics; this leads to new algorithms with priors adapted to supercoiled DNA, taking into account the random nature of the free energies needed to denature base pairs.

Bayes Theorem↗
PubMed2002

A Bayesian fixed effects analysis of the Mantel-Haenszel model applied to meta-analysis.

When performing a meta analysis, it is often necessary to combine results from several 2 x 2 contingency tables. The Mantel-Haenszel model assumes a common measure of association between the treatment and outcome variables across the tables. A Bayesian method is described for drawing inferences regarding the measure of association, for checking the plausibility of the Mantel-Haenszel model, and for drawing inferences regarding the success rates for the individual studies. While the methodology is readily extendable to random effects models, a fixed effects approach avoids the complex statistical modelling of a mixture distribution which is required for the good application of random effects models.

Anti-Bacterial Agents↗
PubMed2006

A future for models and data in environmental science.

Together, graphical models and the Bayesian paradigm provide powerful new tools that promise to change the way that environmental science is done. The capacity to merge theory with mechanistic understanding and empirical evidence, to assimilate diverse sources of information and to accommodate complexity will transform the collection and interpretation of data. As we discuss here, we specifically expect a shift from a focus on simple experiments with inflexible design and selection among models that embrace parts of processes to a synthesis of integrated process models. With this potential come new challenges, including some that are specific and technical and others that are general and will involve reexamination of the role of inference and prediction.

Animals↗
PubMed1999

Model-independent mean-field theory as a local method for approximate propagation of information.

We present a systematic approach to mean-field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean-field equations derived here may serve as a local, and thus very simple, method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. Our approach is 'model-independent' in the sense that we do not assume a particular type of dependences; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependences. In general, there are multiple solutions to the mean-field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean-field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared with the exact results.

Child↗
PubMed2004

Assessment of operative risk in colorectal cancer surgery: the Cleveland Clinic Foundation colorectal cancer model.

INTRODUCTION: Predictive models play a pivotal role in the provision of risk-adjusted, operative mortality rates. The purpose of the study was to describe the development of a dedicated prognostic index for quantifying operative risk in colorectal cancer surgery. METHODS: Data were collected from 5,034 consecutive patients undergoing major surgery in a single center from October 1976 to July 2002. Primary end point was 30-day operative mortality. A multilevel Bayesian logistic regression model was developed to adjust for case-mix and accommodate the variability of outcomes between surgeons. The model was internally validated (split-sample) and tested using measures of discrimination, calibration, and subgroup analysis. RESULTS: The patients' median age was 66 (range, 18-98) years. Operative mortality was 2.3 percent with no significant variability between surgeons or through time. Multivariate analysis identified the following independent risk factors: age (odds ratio = 1.5 per 10-year increase), American Society of Anesthesiologists grade (odds ratio for ASA II, III, IV-V vs. I = 2.6, 4.3, 6.8), TNM staging (odds ratio for Stage IV vs. I-III = 2.6), mode of surgery (odds ratio for urgent vs. nonurgent = 2.1) no-cancer resection vs. cancer resection (odds ratio = 4.5), and hematocrit level. The model offered adequate discrimination (area under receiver operator characteristic curve = 0.801) and excellent agreement between observed and model-predicted outcomes over ten major colorectal procedures (P = 0.191). CONCLUSIONS: The colorectal cancer model provided an accurate means of estimating risk for individual patients in the preoperative setting. It has important implications in everyday practice, because it may be used as an adjunct in the process of informed consent and for monitoring surgical performance through time.

Adolescent↗
PubMed2005

Seasonal analyses of air pollution and mortality in 100 US cities.

Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors developed Bayesian semiparametric hierarchical models for estimating time-varying effects of pollution on mortality in multisite time series studies. The methods were applied to the database of the National Morbidity and Mortality Air Pollution Study, which includes data for 100 US cities, for the period 1987-2000. At the national level, a 10-microg/m(3) increase in particulate matter less than 10 microm in aerodynamic diameter at a 1-day lag was associated with 0.15% (95% posterior interval (PI): -0.08, 0.39), 0.14% (95% PI: -0.14, 0.42), 0.36% (95% PI: 0.11, 0.61), and 0.14% (95% PI: -0.06, 0.34) increases in mortality for winter, spring, summer, and fall, respectively. An analysis by geographic region found a strong seasonal pattern in the Northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.

Air Pollution↗
PubMed2004

Identification of DNA regulatory motifs using Bayesian variable selection.

MOTIVATION: Understanding the mechanisms that determine gene expression regulation is an important and challenging problem. A common approach consists of identifying DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Here, we consider a regression model that linearly relates gene expression levels to a sequence matching score of nucleotide patterns. We use Bayesian models and stochastic search techniques to select transcription factor binding site candidates, as an alternative to stepwise regression procedures used by other investigators. RESULTS: We demonstrate through simulated data the improved performance of the Bayesian variable selection method compared to the stepwise procedure. We then analyze and discuss the results from experiments involving well-studied pathways of Saccharomyces cerevisiae and Schizosaccharomyces pombe. We identify regulatory motifs known to be related to the experimental conditions considered. Some of our selected motifs are also in agreement with recent findings by other researchers. In addition, our results include novel motifs that constitute promising sets for further assessment. AVAILABILITY: The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author.

Algorithms↗
PubMed2000

Correctness of local probability in graphical models with loops.

Graphical models, such as Bayesian networks and Markov networks, represent joint distributions over a set of variables by means of a graph. When the graph is singly connected, local propagation rules of the sort proposed by Pearl (1988) are guaranteed to converge to the correct posterior probabilities. Recently a number of researchers have empirically demonstrated good performance of these same local propagation schemes on graphs with loops, but a theoretical understanding of this performance has yet to be achieved. For graphical models with a single loop, we derive an analytical relationship between the probabilities computed using local propagation and the correct marginals. Using this relationship we show a category of graphical models with loops for which local propagation gives rise to provably optimal maximum a posteriori assignments (although the computed marginals will be incorrect). We also show how nodes can use local information in the messages they receive in order to correct their computed marginals. We discuss how these results can be extended to graphical models with multiple loops and show simulation results suggesting that some properties of propagation on single-loop graphs may hold for a larger class of graphs. Specifically we discuss the implication of our results for understanding a class of recently proposed error-correcting codes known as turbo codes.

Bayes Theorem↗