Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Bayesian computational modeling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 577 records · Page 32Linked to original sources
PubMed1994

The hierarchical Bayesian approach to population pharmacokinetic modelling.

Compartmental models are widely used to model the profile of drug concentrations versus time from administration in an individual subject. Observed concentrations are then modelled as noisy departures from the underlying profile, the latter characterised for each individual by a small number of 'individual parameters'. When a population of individuals is studied, inter-individual variation is modelled by assuming that the individual profile parameters are drawn from a population distribution, the latter characterised by 'population parameters' describing, in effect, a mean population profile and individual variation around it. From a Bayesian statistical perspective, such models fit exactly into the so-called hierarchical modelling framework, which provides a coherent basis for individual and population inferences and prediction, as well as for decision-making (for example, the design of dosage regimens). This paper outlines the hierarchical model framework and describes how the required computations can be carried out in a straightforward manner by a Markov chain Monte Carlo technique known as Gibbs sampling, even when models involve mean-variance relationships and outliers.

Bayes Theorem↗
PubMed2000

Decision-support and intelligent tutoring systems in medical education.

One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.

Artificial Intelligence↗
PubMed2006

Spatio-temporal modeling and analysis of fMRI data using NARX neural network.

This paper presents spatio-temporal modeling and analysis methods to fMRI data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain.

Bayes Theorem↗
PubMedDate not supplied

Qualitative probability versus quantitative probability in clinical diagnosis: a study using a computer simulation.

The use of Bayes' theorem as a diagnostic tool in clinical medicine normally requires an input of exact probability estimates. However, humans tend to think in categories ("likely," "unlikely," etc.) rather than in terms of exact probability. A computer simulation of the presenting features of a case of pelvic infection has been used to compare the effects of quantitative and qualitative probability estimates on the diagnostic accuracy of Bayes' theorem. For the commoner conditions (prior probability greater than or equal to 0.2) the use of a two- or three-category system is virtually equivalent to the use of exact probability. However, uncommon conditions (prior probability less than or equal to 0.03) are completely ignored by the qualitative system. It is concluded that the use of simple categories of probability is acceptable for a Bayesian diagnostic system provided that the target conditions have a relatively high prior probability.

Artificial Intelligence↗
PubMed2003

Bayesian estimation of transcript levels using a general model of array measurement noise.

Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome but suffer from significant levels of measurement noise. We present a rigorous new approach to estimate transcript levels and ratios from one or more gene array experiments, given a model of measurement noise and available prior information. The Bayesian estimation of array measurements (BEAM) technique provides a principled method to identify changes in expression level, combine repeated measurements, or deal with negative expression level measurements. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Instead, it relies on computational techniques that apply to a broad range of models. We use Affymetrix yeast chip data to illustrate the process of developing accurate noise and prior models from existing experimental data. The resulting noise model includes novel features such as heavy-tailed additive noise and a gene-specific bias term. We also verify that the resulting noise and prior models fit data from an Affymetrix human chip set.

Bayes Theorem↗
PubMed2000

Validation of techniques for the prediction of carboplatin exposure: application of Bayesian methods.

OBJECTIVE: Several methods have been developed for the prediction of carboplatin exposure to facilitate pharmacokinetic guided dosing. The aim of this study was to develop and validate sparse data Bayesian methods for the estimation of carboplatin exposure and to validate other commonly applied techniques, such as the Chatelut formula, the Sorensen limited sampling model, and the Calvert formula, in which glomerular filtration rate was estimated with the Cockcroft-Gault, the Jelliffe, and the recently proposed Wright formulas. METHODS: Complete concentration-time curves were available for a total of 43 patients (45 courses) receiving carboplatin (265 or 400 mg/m2/day) in a 1-hour infusion for 4 consecutive days in combination with thiotepa and cyclophosphamide. A population two-compartment model was developed on an index set of 12 courses. The other 33 courses served as validation set. Bayesian estimates were generated with the population parameters by use of either one or two randomly timed samples or two samples at optimal time points determined with the D-optimality theory. RESULTS: The Bayesian methods provided an accurate and precise prediction of the area under the concentration-time curve (bias <4% and precision <18%). The other formulas (Sorensen model, Chatelut, and Calvert with Jelliffe, Cockcroft-Gault, and Wright) resulted in a precision >18%, whereas the Jelliffe formula and the Sorensen model resulted in a bias >12%. CONCLUSION: The applicability of a Bayesian method for the prediction of the carboplatin exposure by use of one or two samples without the necessity for exact timing of infusion duration and sampling was demonstrated. The Bayesian method may be very instrumental to execute pharmacokinetic guided dosing for carboplatin.

Adult↗
PubMed2005

An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding.

Neural spike train decoding algorithms are important tools for characterizing how ensembles of neurons represent biological signals. We present a Bayesian neural spike train decoding algorithm based on a point process model of individual neurons, a linear stochastic state-space model of the biological signal, and a temporal latency parameter. The latency parameter represents the temporal lead or lag between the biological signal and the ensemble spiking activity. We use the algorithm to study whether the representation of position by the ensemble spiking activity of pyramidal neurons in the CA1 region of the rat hippocampus is more consistent with prospective coding, i.e., future position, or retrospective coding, past position. Using 44 simultaneously recorded neurons and an ensemble delay latency of 400 ms, the median decoding error was 5.1 cm during 10 min of foraging in an open circular environment. The true coverage probability for the algorithm's 0.95 confidence regions was 0.71. These results illustrate how the Bayesian neural spike train decoding paradigm may be used to investigate spatio-temporal representations of position by an ensemble of hippocampal neurons.

Algorithms↗
PubMed2004

On missing Data Treatment for degraded video and film archives: a survey and a new Bayesian approach.

Image sequence restoration has been steadily gaining in importance with the increasing prevalence of visual digital media. The demand for content increases the pressure on archives to automate their restoration activities for preservation of the cultural heritage that they hold. There are many defects that affect archived visual material and one central issue is that of Dirt and Sparkle, or "Blotches." Research in archive restoration has been conducted for more than a decade and this paper places that material in context to highlight the advances made during that time. The paper also presents a new and simpler Bayesian framework that achieves joint processing of noise, missing data, and occlusion.

Algorithms↗
PubMed2006

Bayesian variable selection for the analysis of microarray data with censored outcomes.

MOTIVATION: A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions. RESULTS: We demonstrate the performance of the method on simulated examples and on several microarray datasets. For the simulation study, we consider scenarios with large number of noisy variables and different degrees of correlation between the relevant and non-relevant (noisy) variables. We are able to identify the correct covariates and obtain good prediction of the survivor functions. For the microarray applications, some of our selected genes are known to be related to the diseases under study and a few are in agreement with findings from other researchers. AVAILABILITY: The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author. CONTACT: mvannucci@stat.tamu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2006

Improved performance of bayesian solutions for inverse electrocardiography using multiple information sources.

The usual goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface potentials and a mathematical model that relates the sources to the measurements. Due to attenuation and smoothing that occurs in the thorax, the inverse ECG problem is ill-posed and imposition of a priori constraints is needed to combat this ill-posedness. When the problem is posed in terms of reconstructing heart surface potentials, solutions have not yet achieved clinical utility; limitations include the limited availability of good a priori information about the solution and the lack of a "good" error metric. We describe an approach that combines body surface measurements and standard forward models with two additional information sources: statistical prior information about epicardial potential distributions and sparse simultaneous measurements of epicardial potentials made with multielectrode coronary venous catheters. We employ a Bayesian methodology which offers a general way to incorporate these information sources and additionally provides statistical performance analysis tools. In a simulation study, we first compare solutions using one or more of these information sources. Then, we study the effects of varying the number of sparse epicardial potential measurements on reconstruction accuracy. To evaluate accuracy, we used the Bayesian error covariance as well as traditional error metrics such as relative error. Our results show that including even sparsely sampled information from coronary venous catheters can substantially improve the reconstruction of epicardial potential distributions and that a Bayesian framework provides a feasible approach to using this information. Moreover, computing the Bayesian error standard deviations offers a means to indicate confidence in the results even in the absence of validation data.

Action Potentials↗
PubMedDate not supplied

Representation and analysis of medical decision problems with influence diagrams.

Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. They use clinical examples to illustrate the mathematical operations of the influence-diagram-evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphic representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis.

AIDS-Related Opportunistic Infections↗
PubMed2004

Aligned 18S and insect phylogeny.

The nuclear small subunit rRNA (18S) has played a dominant role in the estimation of relationships among insect orders from molecular data. In previous studies, 18S sequences have been aligned by unadjusted automated approaches (computer alignments that are not manually readjusted), most recently with direct optimization (simultaneous alignment and tree building using a program called "POY"). Parsimony has been the principal optimality criterion. Given the problems associated with the alignment of rRNA, and the recent availability of the doublet model for the analysis of covarying sites using Bayesian MCMC analysis, a different approach is called for in the analysis of these data. In this paper, nucleotide sequence data from the 18S small subunit rRNA gene of insects are aligned manually with reference to secondary structure, and analyzed under Bayesian phylogenetic methods with both GTR+I+G and doublet models in MrBayes. A credible phylogeny of Insecta is recovered that is independent of the morphological data and (unlike many other analyses of 18S in insects) not contradictory to traditional ideas of insect ordinal relationships based on morphology. Hexapoda, including Collembola, are monophyletic. Paraneoptera are the sister taxon to a monophyletic Holometabola but weakly supported. Ephemeroptera are supported as the sister taxon of Neoptera, and this result is interpreted with respect to the evolution of direct sperm transfer and the evolution of flight. Many other relationships are well-supported but several taxa remain problematic, e.g., there is virtually no support for relationships among orthopteroid orders. A website is made available that provides aligned 18S data in formats that include structural symbols and Nexus formats.

Animals↗
PubMed2001

Ideal observer approximation using Bayesian classification neural networks.

It is well understood that the optimal classification decision variable is the likelihood ratio or any monotonic transformation of the likelihood ratio. An automated classifier which maps from an input space to one of the likelihood ratio family of decision variables is an optimal classifier or "ideal observer." Artificial neural networks (ANNs) are frequently used as classifiers for many problems. In the limit of large training sample sizes, an ANN approximates a mapping function which is a monotonic transformation of the likelihood ratio, i.e., it estimates an ideal observer decision variable. A principal disadvantage of conventional ANNs is the potential over-parameterization of the mapping function which results in a poor approximation of an optimal mapping function for smaller training samples. Recently, Bayesian methods have been applied to ANNs in order to regularize training to improve the robustness of the classifier. The goal of training a Bayesian ANN with finite sample sizes is, as with unlimited data, to approximate the ideal observer. We have evaluated the accuracy of Bayesian ANN models of ideal observer decision variables as a function of the number of hidden units used, the signal-to-noise ratio of the data and the number of features or dimensionality of the data. We show that when enough training data are present, excess hidden units do not substantially degrade the accuracy of Bayesian ANNs. However, the minimum number of hidden units required to best model the optimal mapping function varies with the complexity of the data.

Bayes Theorem↗
PubMed2002

Disease transmission models for public health decision making: toward an approach for designing intervention strategies for Schistosomiasis japonica.

Mathematical models of disease transmission processes can serve as platforms for integration of diverse data, including site-specific information, for the purpose of designing strategies for minimizing transmission. A model describing the transmission of schistosomiasis is adapted to incorporate field data typically developed in disease control efforts in the mountainous regions of Sichuan Province in China, with the object of exploring the feasibility of model-based control strategies. The model is studied using computer simulation methods. Mechanistically based models of this sort typically have a large number of parameters that pose challenges in reducing parametric uncertainty to levels that will produce predictions sufficiently precise to discriminate among competing control options. We describe here an approach to parameter estimation that uses a recently developed statistical procedure called Bayesian melding to sequentially reduce parametric uncertainty as field data are accumulated over several seasons. Preliminary results of applying the approach to a historical data set in southwestern Sichuan are promising. Moreover, technologic advances using the global positioning system, remote sensing, and geographic information systems promise cost-effective improvements in the nature and quality of field data. This, in turn, suggests that the utility of the modeling approach will increase over time.

Bayes Theorem↗
PubMed1988

Evaluating diagnostic performance of clinical tests by spreadsheet modeling. Bayesian analysis using Ri/Cj ratio as a unifying concept.

We present a general spreadsheet model for evaluating diagnostic performance of clinical tests. Our model depicts test results as an r X c matrix, with r possible test results and c possible clinical states. Analysis of this matrix is based on the Ri/Cj ratio, calculated as a number of subjects having a specified result Ri within a given clinical state Cj, divided by total subjects within this clinical state. From this model, we can identify three special cases: (1) a 2 X c matrix, with two possible test results of T+ or T-, over c possible clinical states; (2) an r X 2 matrix, with r possible test results, over two possible clinical states of D+ or D-; and (3) a 2 X 2 matrix, with two possible test results over two possible clinical states. Application of the Ri/Cj ratio to the r X c matrix provides a useful approach to graphic analysis of multiple test results over multiple clinical states. The Ri/Cj ratio also provides a general approach to Bayesian analysis, in which likelihood ratio, relative operating characteristic analysis, sensitivity, and specificity represent special cases or special applications.

Bayes Theorem↗
PubMed1989

Computer-assisted optimization of aminophylline therapy in the emergency department.

The emergency department (ED) is a unique setting for pharmacokinetic-guided drug administration because of the need to rapidly optimize therapy. We compared outcomes in patients receiving intravenous aminophylline according to population-based ED guidelines (group 1) or Bayesian-derived pharmacokinetic estimates (group 2), we determined predictors for admission or discharge in our study group, and we assessed the ability of a Bayesian pharmacokinetic model to estimate theophylline requirements in the ED. The study population was composed of 82 patients (42 males, 40 females) with a mean age of 43 +/- 15.5 years. Fifteen patients were excluded because of protocol violations. Of the 67 cases studied, 30 were assigned to group 1, and 37 were assigned to group 2. Patient demographics, baseline theophylline concentration, and theophylline loading dose did not differ significantly between treatment groups. The aminophylline maintenance infusion was significantly (P less than .001) lower in group 1 (0.4 +/- 0.2 mg/kg/h) than in group 2 (0.6 +/- 0.2 mg/kg/h). Serum theophylline concentrations at one hour post-loading-dose did not differ significantly between treatment groups; however, significant differences were observed at two hours post-load (P less than .002) and four hours post-load (P less than .001). Baseline peak flow rate (PFR) was significantly (P less than .03) higher in group 1 (170 +/- 85 L/min) than in group 2 (132 +/- 62 L/min), but did not differ significantly at any other times throughout the study. The PFR one hour post-load (PFR-1) was the strongest (P less than .003) predictor of outcome.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed2005

Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data.

We present a method for jointly learning dynamic models of transcriptional regulatory networks from gene expression data and transcription factor binding location data. Models are automatically learned using dynamic Bayesian network inference algorithms; joint learning is accomplished by incorporating evidence from gene expression data through the likelihood, and from transcription factor binding location data through the prior. We propose a new informative structure prior with two advantages. First, the prior incorporates evidence from location data probabilistically, allowing it to be weighed against evidence from expression data. Second, the prior takes on a factorable form that is computationally efficient when learning dynamic regulatory networks. Results obtained from both simulated and experimental data from the yeast cell cycle demonstrate that this joint learning algorithm can recover dynamic regulatory networks from multiple types of data that are more accurate than those recovered from each type of data in isolation.

Bayes Theorem↗
PubMed2001

Predicting analysis times in randomized clinical trials.

Randomized clinical trial designs commonly include one or more planned interim analyses. At these times an external monitoring committee reviews the accumulated data and determines whether it is scientifically and ethically appropriate for the study to continue. With failure-time endpoints, it is common to schedule analyses at the times of occurrence of specified landmark events, such as the 50th event, the 100th event, and so on. Because interim analyses can impose considerable logistical burdens, it is worthwhile predicting their timing as accurately as possible. We describe two model-based methods for making such predictions during the course of a trial. First, we obtain a point prediction by extrapolating the cumulative mortality into the future and selecting the date when the expected number of deaths is equal to the landmark number. Second, we use a Bayesian simulation scheme to generate a predictive distribution of milestone times; prediction intervals are quantiles of this distribution. We illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease.

Bayes Theorem↗