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 451 records · Page 25Linked to original sources
PubMed1996

A knowledge-based model construction approach to medical decision making.

We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Acute Disease↗
PubMed1993

Population pharmacokinetic analysis of new aminoglycosides, astromicin and isepamicin, and evaluation of Bayesian prediction method for approximation of individual clearance of drug.

Pharmacokinetic data obtained from healthy subjects after a single i.v. infusion over 30 min of either astromicin (AST) or isepamicin (ISP), the newly developed aminoglycoside antibiotics, were analyzed by a computer program, NONMEM, together with those in patients having impaired renal functions of various degrees, which were cited from the literature. A two-compartment open model was utilized for the analysis, assuming that the total body clearance of drug (ClB) is linearly correlated with endogeneous creatinine clearance (Clcr). By the analysis, it was found that the body weight explains some part of interindividual variability in ClB of ISP, although it did not hold true in the case of AST. For each drug, the means and variances of ClB and the distribution volume of central compartment, and only the means of two intercompartmental constants (K12 and K21), all of which were obtained by the NONMEM analysis, were implemented in a Bayesian prediction program for a microcomputer. This, thus, clarified a point as to when blood sample should be collected in order to get the best prediction of individual ClB with the use of the above Bayesian program and the measurement of drug concentration in the sampled blood as a feedback information. For this purpose, the drug concentrations in plasma obtained in the multiple-dose study of each drug, in which the drug was administered in healthy subjects as i.v. infusion over 1 h every 12 h for 4.5 days, were used.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed2005

Quantitative risk modelling for new pharmaceutical compounds.

The process of discovering and developing new drugs is long, costly and risk-laden. Faced with a wealth of newly discovered compounds, industrial scientists need to target resources carefully to discern the key attributes of a drug candidate and to make informed decisions. Here, we describe a quantitative approach to modelling the risk associated with drug development as a tool for scenario analysis concerning the probability of success of a compound as a potential pharmaceutical agent. We bring together the three strands of manufacture, clinical effectiveness and financial returns. This approach involves the application of a Bayesian Network. A simulation model is demonstrated with an implementation in MS Excel using the modelling engine Crystal Ball.

Algorithms↗
PubMed2003

Inference and computation with population codes.

In the vertebrate nervous system, sensory stimuli are typically encoded through the concerted activity of large populations of neurons. Classically, these patterns of activity have been treated as encoding the value of the stimulus (e.g., the orientation of a contour), and computation has been formalized in terms of function approximation. More recently, there have been several suggestions that neural computation is akin to a Bayesian inference process, with population activity patterns representing uncertainty about stimuli in the form of probability distributions (e.g., the probability density function over the orientation of a contour). This paper reviews both approaches, with a particular emphasis on the latter, which we see as a very promising framework for future modeling and experimental work.

Animals↗
PubMed1994

Afterimages: integration of diagnostic information through Bayesian enhancement of scintigraphic images.

Although the diagnostic accuracy of myocardial perfusion scintigraphy can be improved by additional consideration of clinical and exercise information, multivariate prediction models are infrequently used for this purpose in the clinical setting. We therefore developed a Bayesian algorithm that instead transforms the scintigraphic image itself, modifying defect contrast as a function of the pretest likelihood of coronary artery disease. The algorithm was tested in computer simulations of myocardial perfusion scintigraphy with data from 378 patients (166 from California and 212 from West Virginia) who underwent planar exercise thallium-201 scintigraphy and coronary angiography. Images were interpreted before and after enhancement by eight readers (four at each medical center) with different training orientations (internist, radiologist, cardiologist, nuclear cardiologist, and nuclear medicine technologist) who used a four-point score (from 0, normal to 3, severe defect). Accuracy was quantified as area under a receiver-operating characteristic (ROC) curve. Improvements in accuracy obtained by the algorithm were compared to those provided by multiple logistic regression. Overall, Bayesian enhancement increased ROC area from 0.63 +/- 0.04 to 0.71 +/- 0.04 (p < 0.01). The improvement was consistent for all 16 reading sets (eight readers multiplied by two patient populations; p < 0.05). In comparison, multiple logistic regression increased ROC area from 0.63 +/- 0.04 to 0.79 +/- 0.03 (p < 0.01), outperforming interpretation of the enhanced images in 13 of the 16 reading sets. Bayesian enhancement improves diagnostic accuracy of conventional scintigraphic image interpretation. The improvement is stable across individuals, training orientations, and patient populations. Although this approach is not as accurate as multiple logistic regression, it may be more practical for widespread clinical application.

Algorithms↗
PubMed2005

A Bayesian approach for applying Haseman-Elston methods.

The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDX1 variable as our response.

Alcoholism↗
PubMed1999

Bayesian inference analysis of ellipsometry data.

Variable angle spectroscopic ellipsometry is a nondestructive technique for accurately determining the thicknesses and refractive indices of thin films. Experimentally, the ellipsometry parameters psi and Delta are measured, and the sample structure is then determined by one of a variety of approaches, depending on the number of unknown variables. The ellipsometry parameters have been inverted analytically for only a small number of sample types. More general cases require either a model-based numerical technique or a series of approximations combined with a sound knowledge of the test sample structure. In this paper, the combinatorial optimization technique of simulated annealing is used to perform least-squares fits of ellipsometry data (both simulated and experimental) from both a single layer and a bilayer on a semi-infinite substrate using what is effectively a model-free system, in which the thickness and refractive indices of each layer are unknown. The ambiguity inherent in the best-fit solutions is then assessed using Bayesian inference. This is the only way to consistently treat experimental uncertainties along with prior knowledge. The Markov chain Monte Carlo algorithm is used. Mean values of unknown parameters and standard deviations are determined for each and every solution. Rutherford backscattering spectrometry is used to assess the accuracy of the solutions determined by these techniques. With our computer analysis of ellipsometry data, we find all possible models that adequately describe that data. We show that a bilayer consisting of a thin film of poly(styrene) on a thin film of silicon dioxide on a silicon substrate results in data that are ambiguous; there is more than one acceptable description of the sample that will result in the same experimental data.

Journal Article↗
PubMed2007

Evaluation of uncertainty parameters estimated by different population PK software and methods.

The uncertainty associated with parameter estimations is essential for population model building, evaluation, and simulation. Summarized by the standard error (SE), its estimation is sometimes questionable. Herein, we evaluate SEs provided by different non linear mixed-effect estimation methods associated with their estimation performances. Methods based on maximum likelihood (FO and FOCE in NONMEM, nlme in Splus, and SAEM in MONOLIX) and Bayesian theory (WinBUGS) were evaluated on datasets obtained by simulations of a one-compartment PK model using 9 different designs. Bootstrap techniques were applied to FO, FOCE, and nlme. We compared SE estimations, parameter estimations, convergence, and computation time. Regarding SE estimations, methods provided concordant results for fixed effects. On random effects, SAEM and WinBUGS, tended respectively to under or over-estimate them. With sparse data, FO provided biased estimations of SE and discordant results between bootstrapped and original datasets. Regarding parameter estimations, FO showed a systematic bias on fixed and random effects. WinBUGS provided biased estimations, but only with sparse data. SAEM and WinBUGS converged systematically while FOCE failed in half of the cases. Applying bootstrap with FOCE yielded CPU times too large for routine application and bootstrap with nlme resulted in frequent crashes. In conclusion, FO provided bias on parameter estimations and on SE estimations of random effects. Methods like FOCE provided unbiased results but convergence was the biggest issue. Bootstrap did not improve SEs for FOCE methods, except when confidence interval of random effects is needed. WinBUGS gave consistent results but required long computation times. SAEM was in-between, showing few under-estimated SE but unbiased parameter estimations.

Bayes Theorem↗
PubMed2003

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis.

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Algorithms↗
PubMed2006

cBrother: relaxing parental tree assumptions for Bayesian recombination detection.

UNLABELLED: Bayesian multiple change-point models accurately detect recombination in molecular sequence data. Previous Java-based implementations assume a fixed topology for the representative parental data. cBrother is a novel C language implementation that capitalizes on reduced computational time to relax the fixed tree assumption. We show that cBrother is 19 times faster than its predecessor and the fixed tree assumption can influence estimates of recombination in a medically-relevant dataset. AVAILABILITY: cBrother can be freely downloaded from http://www.biomath.org/dormanks/ and can be compiled on Linux, Macintosh and Windows operating systems. Online documentation and a tutorial are also available at the site.

Algorithms↗
PubMed2005

Systems biology for cancer.

PURPOSE OF REVIEW: Significant insight can be gained into complex biologic mechanisms of cancer via a combined computational and experimental systems biology approach. This review highlights some of the major systems biology efforts that were applied to cancer in the past year. RECENT FINDINGS: Two main approaches to computational systems biology are discussed: mechanistic dynamical simulations and inferential data mining. Significant developments have occurred in both areas. For example, mechanistic simulations of the EGFR pathway are promoting understanding of cancer, and Bayesian inference approaches allow for the reconstruction of regulatory networks. In addition, the article reports on advancements in experimental systems biology for determining protein-protein interactions and quantifying protein expression to generate the necessary data for computational modeling and inferential data mining. Emerging approaches will further improve the ability to bridge the gap between in vitro systems and in vivo human biology. Technologies paving the way include in vitro models that better reflect in vivo tumors, microfabricated devices of human physiology, and improved animal models. SUMMARY: An important challenge facing the field is how better to translate in vitro discoveries to the clinic. Computational systems biology approaches that use omic data to predict biology along with novel experimental systems that better represent human in vivo biology will prove useful in bridging this gap. Although still early, the potential application of systems biology and the future evolution of the field will significantly affect understanding of cancer disease mechanisms and the ability to devise effective therapeutics.

Computational Biology↗
PubMed2005

Nonlinear statistical modeling and model discovery for cardiorespiratory data.

We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.

Algorithms↗
PubMed2006

Estimating receiver operating characteristic curves with covariates when there is no perfect reference test for diagnosis of Johne's disease.

Paratuberculosis (Johne's disease) is a significant animal health problem. Evaluation of diagnostic tests for Johne's disease has been difficult due to lack of a gold standard test. In recent years, there has been interest in receiver operating characteristic (ROC) curve estimation without any gold standard test. Typically, either Bayesian or maximum likelihood methods are proposed. Although these methods overcome the lack of a gold standard test in ROC curve estimation, little work has been done to incorporate covariates in the analysis. In this paper, we propose a method for estimation of ROC curves based on statistical models to adjust for covariate effects when the true disease states of test animals are unknown. The covariates may be correlated with the disease process or with the diagnostic testing procedure, or both. We propose a 2-part Bayesian model: first, a logistic regression model for disease prevalence is used to fit the covariates; second, a linear model is used to fit the covariates to the distribution of test scores. We used Markov chain Monte Carlo methods to compute the posterior estimates of the sensitivities and specificities that provide the groundwork for inference concerning the diagnostic procedure's accuracy. We applied the methodology to milk ELISA scores from several dairy-cow herds for the diagnostic testing of paratuberculosis. We found that both milk yield and its interaction with age had significant effects on the disease process whereas only milk yield was significant on the testing procedure.

Animals↗
PubMed1991

Nomogram for dosing warfarin at steady state.

The predictive performance of a nomogram for dosing warfarin was compared with that of a computer program. The nomogram and the computer program were developed from the log-linear model describing warfarin pharmacodynamics at steady state. The nomogram's dose-response curves were generated by using previously reported pharmacodynamic and pharmacokinetic values for an outpatient population receiving warfarin. The series of dose-response curves were plotted by altering the pharmacodynamic values over a range of 3 standard deviations. The ability of the nomogram to predict the steady-state prothrombin time ratio (PTR) after an adjustment in the dosage of warfarin was evaluated, and the results were compared with those of a commercially available program involving Bayesian regression. Data for 65 outpatients were evaluated. The mean +/- S.D. nomogram-predicted, computer-predicted, and measured PTRs were 1.63 +/- 0.27, 1.64 +/- 0.24, and 1.66 +/- 0.23, respectively. The mean prediction errors for the nomogram and the computer program were -0.037 and -0.026, respectively, and the mean percent absolute prediction errors were 11.6% and 11.0%, respectively. Neither method was biased, and differences between the results for the two methods were not significant. The predictive performance of the warfarin dosing nomogram was comparable to that of the computer program.

Bayes Theorem↗
PubMed2005

Population pharmacokinetics of efavirenz in an unselected cohort of HIV-1-infected individuals.

OBJECTIVE: The aim of this study was to characterise the population pharmacokinetics of efavirenz in a representative patient population and to identify patient characteristics influencing the pharmacokinetics of efavirenz, with the ultimate goal of further developing techniques that can be applied to optimise therapeutic drug monitoring of antiretroviral agents. METHODS: Ambulatory HIV-1-infected patients using an efavirenz-containing regimen were included. During regular visits, blood samples were collected for efavirenz plasma concentrations and clinical chemistry parameters. Concentrations of efavirenz were quantitatively assessed by a validated high-performance liquid chromatographic with ultraviolet detection method. Using nonlinear mixed-effect modelling (NONMEM), the pharmacokinetics of efavirenz were described. Disposition of efavirenz was described by a two-compartment model and absorption was modelled using a chain of three transition compartments. Apparent clearance (CL/F), volume of distribution after oral administration (V(d)/F), intercompartmental clearance, the peripheral volume of distribution and the intercompartmental transition rate constant (k(tr)) were estimated. Furthermore, interindividual, interoccasion and residual variability were estimated. The influence of patient characteristics on the pharmacokinetic parameters of efavirenz was explored. RESULTS: From 172 patients, 40 full pharmacokinetic curves and 315 efavirenz plasma concentrations at a single timepoint were available, resulting in a database of 1009 efavirenz plasma concentrations. CL/F, V(d)/F, and k(tr) were 11.7 L/h (4.3% relative standard error [RSE]), 189L (14.6% RSE) and 3.07 h(-1) (11.2% RSE), respectively. Residual variability in the model was composed of 0.14 mg/L additive error and 8.85% proportional error. Asian race and baseline total bilirubin (TBR) increased the relative bioavailability of efavirenz by 56% and 57%, respectively. No significant covariates were found for CL/F or V(d)/F. CONCLUSION: The pharmacokinetic parameters of efavirenz were adequately described with the developed population pharmacokinetic model. Asian race and baseline TBR were found to be significantly correlated with the bioavailability of efavirenz. The described model will be an essential tool in further optimisation of efavirenz-containing antiretroviral therapy, e.g. by the use of Bayesian estimation of individual pharmacokinetic parameters.

Absorption↗
PubMed2002

A quantitative framework for a multi-group model of Schistosomiasis japonicum transmission dynamics and control in Sichuan, China.

A quantitative framework is presented for the site-specific characterization of schistosomiasis transmission with the object of developing local control strategies. Central to the framework is a worm-burden model using ordinary differential equations of disease transmission in risk groups defined by residence and occupation. The model incorporates temperature- and precipitation-dependent seasonality of infectious stages, snail population dynamics, and seasonal patterns of human water contact specific to the local agricultural setting. The model's parameters are separated into two main subsets, those associated with the general biology of the parasite and its life cycle in the human and the snail and those associated with directly measurable features of disease status in the local population or relevant aspects of the local environment. In this regard, the model is structured and parameterized to take maximum advantage of data that can be collected in rural China by conventional methods. For example, it includes a statistical model for egg excretion to the environment by each risk group which is based on local population surveys of the prevalence and intensity of infection. The second element of the framework of analysis relates to the strategy for parameter estimation and calibration to local conditions. We propose a Bayesian approach in which parameter estimates are refined over time by methods employing extensive computer simulations. An early analysis of data collected between 1987 and 1989 in endemic villages near Xichang City in southwestern Sichuan provides encouragement that parametric uncertainty can be reduced to levels adequate to explore effective control strategies.

Animals↗
PubMed2006

Multi-class cancer classification using multinomial probit regression with Bayesian gene selection.

We consider the problems of multi-class cancer classification from gene expression data. After discussing the multinomial probit regression model with Bayesian gene selection, we propose two Bayesian gene selection schemes: one employs different strongest genes for different probit regressions; the other employs the same strongest genes for all regressions. Some fast implementation issues for Bayesian gene selection are discussed, including preselection of the strongest genes and recursive computation of the estimation errors using QR decomposition. The proposed gene selection techniques are applied to analyse real breast cancer data, small round blue-cell tumours, the national cancer institute's anti-cancer drug-screen data and acute leukaemia data. Compared with existing multi-class cancer classifications, our proposed methods can find which genes are the most important genes affecting which kind of cancer. Also, the strongest genes selected using our methods are consistent with the biological significance. The recognition accuracies are very high using our proposed methods.

Algorithms↗