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At least 883 records · Page 49Linked to original sources
PubMed1990

Bayesian estimation of cyclosporine clearance in bone marrow graft.

The dosage regimen of cyclosporine (CsA) can be individualized in patients by means of a test dose (TD) method in the few days before bone marrow graft. To simplify the test dosing protocol, we used a Bayesian estimation (BE) of CsA clearance requiring population data and partial kinetic information for a given patient. In the first part, 42 patients, aged 11-47 years, were given a 2-h infusion of CsA (4 mg.kg-1). CsA concentrations were determined in several blood samples. The obtained concentration-time curves were fitted according to a three-compartment model. Maximum likelihood estimation (MLE) allowed CsA determination of pharmacokinetic parameters. Early population parameters of CsA were determined using 22 TD by a two-stage method. In the remaining 20 patients, individual pharmacokinetic parameters were also computed by BE procedure, taking into account the above determined population parameters and CsA concentration determinations from three blood samples drawn at 5 and 30 min and 3 h after the end of TD infusion. In the second part, 16 patients were used to evaluate performance of BE in directly predicting target concentration values. Finally, early population characteristics were updated on the basis of 42 patients. Statistical comparisons between MLE and BE estimates of CsA clearance and between concentrations predicted after BE and those experimentally obtained showed that three blood CsA determinations allowed accurate clearance estimation and target concentration predictions. The method presented here can be used to calculate an individual CsA dosage regimen in real time, thus improving patient comfort.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2002

Source-detector calibration in three-dimensional Bayesian optical diffusion tomography.

Optical diffusion tomography is a method for reconstructing three-dimensional optical properties from light that passes through a highly scattering medium. Computing reconstructions from such data requires the solution of a nonlinear inverse problem. The situation is further complicated by the fact that while reconstruction algorithms typically assume exact knowledge of the optical source and detector coupling coefficients, these coupling coefficients are generally not available in practical measurement systems. A new method for estimating these unknown coupling coefficients in the three-dimensional reconstruction process is described. The joint problem of coefficient estimation and three-dimensional reconstruction is formulated in a Bayesian framework, and the resulting estimates are computed by using a variation of iterative coordinate descent optimization that is adapted for this problem. Simulations show that this approach is an accurate and efficient method for simultaneous reconstruction of absorption and diffusion coefficients as well as the coupling coefficients. A simple experimental result validates the approach.

Bayes Theorem↗
PubMed2003

Genetic analysis of longitudinal trajectory of clinical mastitis in first-lactation Norwegian cattle.

Clinical mastitis records for 36,178 first-lactation daughters of 245 Norwegian Cattle (NRF) sires were analyzed with a Bayesian longitudinal threshold model. For each cow, the period going from 30 d before calving to 300 d after calving was divided into 11 intervals of 30 d length each. Absence or presence of clinical mastitis within each interval was scored as "0" or "1", respectively. A Bayesian threshold model consisting of a set of explanatory variables plus Legendre polynomials on time of order four was used to describe the trajectory of liability to clinical mastitis. Heritability ranged between 0.07 and 0.13 before calving, from 0.04 to 0.15 during the first 270 d after calving, and increased sharply thereafter, as a consequence of the form of the polynomial. Genetic correlations between adjacent days were close to 1, and decreased when days were further apart. Most genetic correlations were moderate to high. A measure of probability of future daughters contracting clinical mastitis during lactation was computed for each sire. A typical curve had a peak near calving followed by a decrease thereafter. The best sires had a low peak around calving and a low expected probability of mastitis among daughters throughout lactation. Expected fraction of days without mastitis was derived from the probability curves and used for ranking of sires. Rank correlations with genetic evaluations of sires obtained from cross-sectional models were high. However, sire selection was affected markedly, especially at high selection intensity. An advantage of the longitudinal model for clinical mastitis is its ability to take multiple treatments and time aspects into account.

Animals↗
PubMed1999

Binary QSAR: a new method for the determination of quantitative structure activity relationships.

A new method (particularly suited to the analysis of High Throughput Screening data) is presented for the determination of quantitative structure activity relationships. The method, termed "Binary QSAR," accepts binary activity measurements (e.g., pass/fail or active/inactive) and molecular descriptor vectors as input. A Bayesian inference technique is used to predict whether or not a new compound will be active or inactive. Experiments were conducted on a data set of 1947 molecules. The results show that the method exhibits high accuracy and is robust to measurement errors.

Bayes Theorem↗
PubMed2005

Bayesian inference on the patient population size given list mismatches.

In applying capture-recapture methods for closed populations to epidemiology, one needs to estimate the total number of people with a certain disease in a certain research area by using several lists with information of patients. Problems of lists error often arise due to mistyping or misinformation. Adopting the concept of tag-loss methodology in animal populations, Seber et al. (Biometrics 2000; 56:1227-1232) proposed solutions to a two-list problem. This article reports an interesting simulation study, where Bayesian point estimates based on improper constant and Jeffreys prior for unknown population size N could have smaller frequentist standard errors and MSEs compared to the estimates proposed in Seber et al. (2000). The Bayesian credible intervals based on the same priors also have super frequentist coverage probabilities while some of the frequentist confidence intervals procedures have drastically poor coverage. Seber's real data set on gestational diabetics is analysed with the proposed new methods.

Bayes Theorem↗
PubMed1996

Machine learning in prognosis of the femoral neck fracture recovery.

We compare the performance of several machine learning algorithms in the problem of prognostics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability.

Algorithms↗
PubMed2001

Three-dimensional Bayesian optical image reconstruction with domain decomposition.

Most current efforts in near-infrared optical tomography are effectively limited to two-dimensional reconstructions due to the computationally intensive nature of full three-dimensional (3-D) data inversion. Previously, we described a new computationally efficient and statistically powerful inversion method APPRIZE (automatic progressive parameter-reducing inverse zonation and estimation). The APPRIZE method computes minimum-variance estimates of parameter values (here, spatially variant absorption due to a fluorescent contrast agent) and covariance, while simultaneously estimating the number of parameters needed as well as the size, shape, and location of the spatial regions that correspond to those parameters. Estimates of measurement and model error are explicitly incorporated into the procedure and implicitly regularize the inversion in a physically based manner. The optimal estimation of parameters is bounds-constrained, precluding infeasible values. In this paper, the APPRIZE method for optical imaging is extended for application to arbitrarily large 3-D domains through the use of domain decomposition. The effect of subdomain size on the performance of the method is examined by assessing the sensitivity for identifying 112 randomly located single-voxel heterogeneities in 58 3-D domains. Also investigated are the effects of unmodeled heterogeneity in background optical properties. The method is tested on simulated frequency-domain photon migration measurements at 100 MHz in order to recover absorption maps owing to fluorescent contrast agent. This study provides a new approach for computationally tractable 3-D optical tomography.

Bayes Theorem↗
PubMed2006

Sequential Monte Carlo for Bayesian matching of objects with occlusions.

We consider the problem of locating instances of a known object in a novel scene by matching the fiducial features of the object. The appearance of the features and the shape of the object are modeled separately and combined in a Bayesian framework. In this paper, we present a novel matching scheme based on Sequential Monte Carlo, in which the features are matched sequentially, utilizing the information about the locations of previously matched features to constrain the task. The particle representation of hypotheses about the object position allow matching in multimodal and cluttered environments, where batch algorithms may have convergence difficulties. The proposed method requires no initialization or predetermined matching order, as the sequence can be started from any feature. We also utilize a Bayesian model to deal with features that are not detected due to occlusions or abnormal appearance. In our experiments, the proposed matching system shows promising results, with performance equal to batch approaches when the target distribution is unimodal, while surpassing traditional methods under multimodal conditions. Using the occlusion model, the object can be localized from only a few visible features, with the nonvisible parts predicted from the conditional prior model.

Algorithms↗
PubMed2006

Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.

MOTIVATION: Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable. RESULTS: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices.

Bayes Theorem↗
PubMed2004

Bayesian estimation of vaccine efficacy.

Protective vaccine efficacy, VEs, is measured as one minus the incidence rate ratio (IRR) or the relative risk (RR) in the vaccinated group compared with the unvaccinated group. In this paper, we systematically present Bayesian estimation of protective vaccine efficacy based on the Poisson and binomial distributions. We also propose a new tool, the vaccine efficacy acceptability curve, to represent the uncertainty for the estimate of the vaccine efficacy graphically. It is very useful, especially when there is no universal agreement on the acceptable vaccine efficacy. The vaccine efficacy acceptability curve is defined as the posterior probability that the measure of vaccine efficacy VEs > or = k for each acceptable value k. When a vaccine is highly efficacious, the number of vaccinated susceptibles being infected is likely to be very small or even zero. Then the assumptions of normality and log-normality of IRR or RR usually do not hold well. Although frequentist exact methods provide good estimates of the confidence interval, they are overly conservative and are computationally difficult to extend to estimate the vaccine efficacy acceptability curve. In this paper, our focus is on Bayesian estimation of protective vaccine efficacy, its highest probability density credible set, and the vaccine efficacy acceptability curve through Markov chain Monte Carlo (MCMC) methods. We illustrate the methods using the data from two pertussis vaccine studies and the H. influenza Type B preventive trial.

Bayes Theorem↗
PubMed1993

Development of a population pharmacokinetic database for tianeptine.

Two thousand three hundred and thirty five plasma concentrations of tianeptine from 112 patients enrolled in nine studies of tianeptine pharmacokinetics performed prior to the marketing of the drug were pooled for analysis using mixed-effect modeling. Studies represented a combination of single dose and multiple dosing at steady-state. Tianeptine plasma concentration time data were fit to a two compartment model with first order absorption using the NONMEM computer program. The results of this analysis suggested that alcoholism is associated with significant increase in clearance (124% increase) and volume of the central compartment (161% increase). The volume of the peripheral compartment is significantly lower in women (31% decrease) and in depressed patients (59% decrease). The population mean (interindividual variability) clearance was equal to 0.17 l.h-1 x kg-1 (28.6%), the volume of central compartment was 0.13 l.kg-1 (60.4%), intercompartmental clearance was 0.07 l.h-1 x kg-1 (30.1%), volume of the tissue compartment was 1.17 l.kg-1 (28.3%), and the absorption rate constant was 0.63 h-1 (21.8%). The residual variability was approximately 30% at concentrations expected during clinical use of the drug. Because of the increased clearance, alcoholic patients would be expected to have significantly reduced concentrations during steady-state dosing. These population parameters provide a basis for developing initial dosing recommendations and for performing bayesian evaluations of drug concentrations obtained in post-marketing studies.

Administration, Oral↗
PubMed2006

Tree and rate estimation by local evaluation of heterochronous nucleotide data.

MOTIVATION: Heterochronous gene sequence data is important for characterizing the evolutionary processes of fast-evolving organisms such as RNA viruses. A limited set of algorithms exists for estimating the rate of nucleotide substitution and inferring phylogenetic trees from such data. The authors here present a new method, Tree and Rate Estimation by Local Evaluation (TREBLE) that robustly calculates the rate of nucleotide substitution and phylogeny with several orders of magnitude improvement in computational time. METHODS: For the basis of its rate estimation TREBLE novelly utilizes a geometric interpretation of the molecular clock assumption to deduce a local estimate of the rate of nucleotide substitution for triplets of dated sequences. Averaging the triplet estimates via a variance weighting yields a global estimate of the rate. From this value, an iterative refinement procedure relying on statistical properties of the triplets then generates a final estimate of the global rate of nucleotide substitution. The estimated global rate is then utilized to find the tree from the pairwise distance matrix via an UPGMA-like algorithm. RESULTS: Simulation studies show that TREBLE estimates the rate of nucleotide substitution with point estimates comparable with the best of available methods. Confidence intervals are comparable with that of BEAST. TREBLE's phylogenetic reconstruction is significantly improved over the other distance matrix method but not as accurate as the Bayesian algorithm. Compared with three other algorithms, TREBLE reduces computational time by a minimum factor of 3000. Relative to the algorithm with the most accurate estimates for the rate of nucleotide substitution (i.e. BEAST), TREBLE is over 10,000 times more computationally efficient. AVAILABILITY: jdobrien.bol.ucla.edu/TREBLE.html

Chromosome Mapping↗
PubMed2004

An integrated comprehensive workbench for inferring genetic networks: voyagene.

We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.

Algorithms↗
PubMed2003

Statistical smoothing of neuronal data.

The purpose of smoothing (filtering) neuronal data is to improve the estimation of the instantaneous firing rate. In some applications, scientific interest centres on functions of the instantaneous firing rate, such as the time at which the maximal firing rate occurs or the rate of increase of firing rate over some experimentally relevant period. In others, the instantaneous firing rate is needed for probability-based calculations. In this paper we point to the very substantial gains in statistical efficiency from smoothing methods compared to using the peristimulus-time histogram (PSTH), and we also demonstrate a new method of adaptive smoothing known as Bayesian adaptive regression splines (DiMatteo 1, Genovese C R and Kass R E 2001 Biometrika 88 1055-71). We briefly review additional applications of smoothing with non-Poisson processes and in the joint PSTH for a pair of neurons.

Action Potentials↗
PubMed2006

LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters.

UNLABELLED: We present a Markov chain Monte Carlo coalescent genealogy sampler, LAMARC 2.0, which estimates population genetic parameters from genetic data. LAMARC can co-estimate subpopulation Theta = 4N(e)mu, immigration rates, subpopulation exponential growth rates and overall recombination rate, or a user-specified subset of these parameters. It can perform either maximum-likelihood or Bayesian analysis, and accomodates nucleotide sequence, SNP, microsatellite or elecrophoretic data, with resolved or unresolved haplotypes. It is available as portable source code and executables for all three major platforms. AVAILABILITY: LAMARC 2.0 is freely available at http://evolution.gs.washington.edu/lamarc

Bayes Theorem↗
PubMed2003

Comparison of Bayesian and maximum likelihood bootstrap measures of phylogenetic reliability.

Owing to the exponential growth of genome databases, phylogenetic trees are now widely used to test a variety of evolutionary hypotheses. Nevertheless, computation time burden limits the application of methods such as maximum likelihood nonparametric bootstrap to assess reliability of evolutionary trees. As an alternative, the much faster Bayesian inference of phylogeny, which expresses branch support as posterior probabilities, has been introduced. However, marked discrepancies exist between nonparametric bootstrap proportions and Bayesian posterior probabilities, leading to difficulties in the interpretation of sometimes strongly conflicting results. As an attempt to reconcile these two indices of node reliability, we apply the nonparametric bootstrap resampling procedure to the Bayesian approach. The correlation between posterior probabilities, bootstrap maximum likelihood percentages, and bootstrapped posterior probabilities was studied for eight highly diverse empirical data sets and were also investigated using experimental simulation. Our results show that the relation between posterior probabilities and bootstrapped maximum likelihood percentages is highly variable but that very strong correlations always exist when Bayesian node support is estimated on bootstrapped character matrices. Moreover, simulations corroborate empirical observations in suggesting that, being more conservative, the bootstrap approach might be less prone to strongly supporting a false phylogenetic hypothesis. Thus, apparent conflicts in topology recovered by the Bayesian approach were reduced after bootstrapping. Both posterior probabilities and bootstrap supports are of great interest to phylogeny as potential upper and lower bounds of node reliability, but they are surely not interchangeable and cannot be directly compared.

Animals↗
PubMed2003

Incorporating biological knowledge into evaluation of causal regulatory hypotheses.

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.

Algorithms↗
PubMed1997

BANTER: a Bayesian network tutoring shell.

We present an educational tool for bringing the information contained in a Bayesian network to the end user in an easily intelligible form. The BANTER shell is designed to tutor users in evaluation of hypotheses and selection of optimal diagnostic procedures. BANTER can be used with any Bayesian network containing nodes that can be classified into hypotheses, observations, and diagnostic procedures. The system enables one to present various types of queries to the network, to test one's ability to select optimal diagnostic procedures, and the request explanations. We describe the system's capabilities by illustrating how it functions with two structurally different network models of real-world medical problems.

Adult↗